AI/ML Engineer | 300K+ Impressions | Python & Full-Stack Developer | Building Next-Gen AI Agents 🤖 | Mentor Empowering Students & IT Pros | Passionate About Smart, Scalable Systems

As an AI/ML Engineer and passionate Python developer, I specialize in building intelligent, scalable systems that solve real-world problems. With a strong foundation in Electrical and Electronics Engineering (B.E., CGPA 8.7), I transitioned into the world of artificial intelligence, machine learning, and full-stack development — driven by curiosity, creativity, and a deep commitment to continuous learning.

At Avasoft, I’ve had the opportunity to work on cutting-edge projects, including AI-powered chatbots, voice-based assistants, RAG (Retrieval-Augmented Generation) models, and real-time backend services using FastAPI, Flask, and PyTorch. I’ve also led data automation efforts through web scraping, vector similarity searches, and NLP-driven applications, deploying robust solutions via Docker and CI/CD pipelines.

Beyond engineering, I find great fulfillment in mentoring and uplifting students and IT professionals. I actively guide aspiring engineers and developers through technical challenges, career planning, and project development. Whether it's through code reviews, workshops, or one-on-one sessions, I believe in creating opportunities for others to grow and succeed in tech.

My strengths lie in end-to-end solution design, a deep understanding of modern AI architectures, and the ability to translate business needs into technical deliverables. I’m always excited to collaborate, share ideas, and innovate in the intersection of AI, software, and real-time systems.

Let’s connect if you’re looking to discuss AI, collaborative projects, or mentoring opportunities!

🚨 STOP Googling “Best AI Books”
You don’t need 1,000 books.
You need these 9.

If you’re serious about AI, ML, and LLMs in 2026, this is the only reading stack that actually matters.
Amazon is noisy.
This list is signal.

9 (Essential) AI / ML Books You Can’t Miss
1️⃣ AI Engineering – Chip Huyen
How real ML systems are built, shipped, and scaled (not Kaggle demos).
🔗 https://lnkd.in/gDNusNaZ

2️⃣ Prompt Engineering for LLMs – Albert Ziegler & John Berryman
The why + how of prompting beyond copy-paste hacks.
🔗 https://lnkd.in/gFy7UfWn

3️⃣ Hands-On Large Language Models – Jay Alammar & Maarten Grootendorst
The clearest mental model for LLMs I’ve seen.
🔗 https://lnkd.in/gJVrtbbq

4️⃣ LLM Engineer’s Handbook – Paul Iusztin & Maxime Labonne
From experiments → production-grade LLM systems.
🔗 https://lnkd.in/gbHrBKmn

5️⃣ Building LLMs from Scratch – Sebastian Raschka, PhD
If you want to truly understand LLMs, start here.
🔗 https://lnkd.in/gESJ9hJ6

6️⃣ Building LLMs for Production – Louis-François Bouchard & Louie Peters
Latency, cost, monitoring, failures — the real problems.
🔗 https://lnkd.in/gJM2vcHT

7️⃣ Designing Machine Learning Systems – Chip Huyen
A must-read before you call anything “production ready.”
🔗 https://lnkd.in/gZrz6iba

8️⃣ Deep Learning – Goodfellow, Bengio & Courville
The DL bible. Hard. Worth it.
🔗 https://lnkd.in/gFzvz3QW

9️⃣ Mathematics for Machine Learning – Deisenroth, Faisal & Ong
The math that quietly separates builders from button-clickers.
🔗 https://lnkd.in/gtjpSrGr

🎯 Why this list matters
Most people:
 Read random blogs
 Watch endless tutorials
Never build real systems

These books:
✅ Teach foundations
✅ Explain production reality
✅ Scale with you from beginner → expert
📌 Save this  your future self will thank you
♻️ Repost to help an AI dev who’s learning the hard way
💬 Comment “BOOKS” I’ll DM you how I’d read these in order

Reshma S

Director @lumay.ai | 1M+ Impressions | Helping Students professional to Build Next-Gen AI Agents | Mentor Empowering Students & IT Pros | Passionate About Smart, Scalable SystemsD

🚨 BREAKING: Zero → AI Engineer in 6 Months (Not by Watching Tutorials)
Most people ask me this:

Where do I actually PRACTICE GenAI instead of just watching videos?
Here’s the honest answer 👇
You don’t learn AI by jumping between random tools.
You learn it by building systems in the right order.
So here’s a 6-month roadmap that combines
Stanford-level theory
 Real ML thinking
 Hands-on GenAI + Agents
Production mindset

🗓️ Month 1 – Core Foundations (No shortcuts)
Before GenAI, you need engineering basics.
• Python (data structures, OOP)
• Math for ML (Linear Algebra, Probability)
• How models actually work
Start here (beginner friendly):
👉 https://lnkd.in/gdSCtXJv
Output: Simple ML scripts + GitHub repo

🗓️ Month 2 – Machine Learning Core
This is where most people quit and where real engineers are made.
• Supervised & unsupervised learning
• Feature engineering
• Model evaluation & bias
🎓 Stanford CS229 – Andrew Ng
👉 https://lnkd.in/gDc9siUC
Output: End-to-end ML project (data → model → evaluation)

🗓️ Month 3 – Deep Learning Systems
Now you stop “using models” and start understanding them.
• Neural networks, CNNs, RNNs
• Training deep models properly
• Debugging learning failures
🎓CS230 – Deep Learning
👉 https://lnkd.in/guaavVdk
CS231n – Vision + Generative Models
👉 https://lnkd.in/gVJZ-xMq
Output: Deep learning project with real training logic

🗓️ Month 4 – LLMs & Generative AI
This is where GenAI finally makes sense.
• Transformers & attention
• Embeddings & vector search
• NLP foundations
 CS224N – NLP with Deep Learning
👉 https://lnkd.in/gQdFAZXM
 CS224U – Language Understanding
👉 https://lnkd.in/gRsWMfA6
Practice here (real interview-level GenAI repo):
👉 https://lnkd.in/gRb53YAF
Output: LLM-powered system (not just prompts)

🗓️ Month 5 – AI Systems & MLOps
This is the difference between demos and production.
• Model deployment (APIs)
• Vector databases
• Monitoring & evaluation
• CI/CD for ML
 Build production-style AI systems, not notebooks
Output: Deployed AI system with monitoring

🗓️ Month 6 – Real-World AI Engineering
Now you think like an AI engineer, not a learner.
• Data pipelines
• Scale & performance
• Security & privacy
• Case studies (fintech, healthcare, SaaS)
 CS221 – Artificial Intelligence
👉 https://lnkd.in/gM9ndUCf
 CS234 – Reinforcement Learning (Agents mindset)
👉 https://lnkd.in/gVvz87Hc

Join the learning community 👉 https://lnkd.in/gyhqiuPG

Most people rush to tools.
Great AI engineers master foundations, then build systems that survive production.”
♻️ Repost to help someone learn AI the right way
📌 Save this  you’ll come back to it
➕ Follow me for real AI engineering & agent systems

Stop building toy AI apps.
Start engineering real AI systems.
Most “AI Engineering” tutorials stop at:
import openai
That’s not engineering.
That’s a demo.
Production is where the real pain begins:
Latency
Context limits
Hallucinations
Tool chaos
Evaluation gaps
Scaling issues
I spent days reading a 382-page AI Engineering handbook, and here’s what actually matters 👇

🚨 The biggest mistake builders make
They obsess over prompts.
Prompts matter 
but context matters more.
If your system can’t manage context, memory, tools, and evaluation,
no prompt will save you.
 What I learned (that most tutorials skip)

1️⃣ RAG fails alone
Static retrieval breaks.
Real systems need:
→ Dynamic agent flow
→ Multiple data sources
→ Smart retrieval strategies

2️⃣ Memory separates demos from products
Good agents don’t just respond, they remember.
→ Short-term = continuity
→ Long-term = personalization
→ Entity-level = real intelligence

3️⃣ MCP kills integration chaos
→ Standard tool protocol
→ No messy glue code
→ Works across systems

4️⃣ Evaluation needs depth
Accuracy ≠ quality.
You need:
→ Component-level traces
→ Multi-turn testing
→ Red-teaming for safety

5️⃣ Production needs optimization
This is where real engineering starts:
→ KV caching for speed
→ Continuous batching for scale
→ Smart deployment choices
Frameworks will change.
These patterns won’t.
If you’re serious about building AI systems (not demos),
this mindset shift matters.

🔖 Save this for later
♻️ Reposting for builders who want to ship production-grade AI, not tutorials.

Join the learning community -https://lnkd.in/gyhqiuPG

#AIEngineering #ArtificialIntelligence #SystemDesign #LLM #RAG #Agents #MCP #SoftwareEngineering #OpenSource

AI Engineering Book

Big Update. Bigger Responsibility. Bigger Vision.

I’m happy to share that I’m starting a new position as Director of AI Product Strategy & GTM at LuMay AI!

What excites me most about this role is the opportunity to:
1.Turn AI ideas into products people actually use
2.Bring clarity between tech, product, and business
3. Build AI that’s practical, scalable, and meaningful

I’m thankful to everyone who supported, challenged, and believed in me along the way.

A special thank you to Palanisamy, CEO of LuMay AI, for the trust and confidence in me. Truly grateful for the opportunity to build and scale something impactful together.

undefined undefined

Director of AI Product Strategy & GTM

AI/ML  Engineer

Director @lumay.ai | 1M+ Impressions | Helping Students professional to Build Next-Gen AI Agents  | Mentor Empowering Students & IT Pros | Passionate About Smart, Scalable SystemsD

LuMay AI

Founder & CEO, LuMay AI | 25+ Years Scaling Enterprise AI | Helped Companies Cut AI Execution Time by 85%+ | Agentic AI • Multi-Agent Systems • Voice Agents

📌 This Is Why Your LLM Hallucinates (And How RAG Fixes It)

Most people say “RAG reduces hallucinations.
But this diagram shows WHY.

Let’s walk through what’s happening 👇
 What you’re seeing here is Retrieval-Augmented Generation (RAG)
RAG = LLM + External Knowledge + Retrieval Logic
Instead of guessing from training memory, the model:
 searches
retrieves
then answers
 Step-by-step (mapped to the GIF)

1️⃣ Indexing phase (Top flow)
Your data (PDFs, docs, DBs, websites)
Broken into chunks
Converted into embeddings (vectors)
Stored inside a vector database
This is how your knowledge becomes searchable by meaning, not keywords.

2️⃣ Retrieval phase (Right side)
User asks a text query
Query → embedding
Similarity search runs in the vector DB
Most relevant chunks are fetched
This is the Retriever.

3️⃣ Generation phase (Center)
Retrieved context + user question
Passed to the Language Model
Model generates an answer grounded only in retrieved data
This is the Generator.

❌ Without RAG (Bottom-left)
LLM says:
“I don’t have information…”
or worse… hallucinates.

✅ With RAG (Bottom-left)
LLM answers using:
Real documents
Recent events
Verifiable sources
Same model.
Different system.

💡 Why RAG is a 2026 default
✅ Fewer hallucinations
✅ Transparent & traceable answers
✅ Works beyond training cut-offs
✅ Required for enterprise, healthcare, legal, internal tools

🧩 Key takeaway
RAG isn’t a feature.
It’s an architecture.
If your AI can’t retrieve, it can’t be trusted.

🔁 Repost  this visual alone can save months of confusion.
✨ Follow Reshma S | #ReshmaWithAI for practical GenAI, RAG, and agentic AI insights.

#RAG #RetrievalAugmentedGeneration #AgenticRAG #GenAI #LLM #VectorDatabase #AIArchitecture #AIBuilders #AICommunity #LearnWithReshma #TechTrends #ReshmaWithAI

🚨 STOP SCROLLING --> AI/ML INTERVIEW GOLD 🚨

If you’re preparing for AI / ML interviews in 2025, this is NOT optional.
I’ve curated 25 MUST-KNOW AI-ML Interview Questions  explained clearly, practically, and industry-ready.
 This carousel covers:
✔️ Core AI vs ML fundamentals
✔️ Supervised, Unsupervised & Reinforcement Learning
✔️ Bias–Variance tradeoff
✔️ Feature Engineering & Regularization
✔️ Gradient Descent, Metrics & Model Evaluation
✔️ Neural Networks, CNNs, RNNs
✔️ Transformers, NLP, GANs
✔️ Real-world interview use cases

Whether you’re a:
Student
Fresher
 Working professional
 Career switcher into AI/ML

This is your interview survival kit.
📥 Save this post you’ll thank yourself later
🔁 Repost and help someone crack their AI dream job
  Follow Reshma S | #ReshmawithAI for more Ai insights

join the learning community-https://lnkd.in/gNtKQa2G 

#AI #MachineLearning #AIInterview #MLInterview #DataScience #Freshers #CareerGrowth #LuMayAI #ReshmawithAI #LinkedInLearning

Interview Questions For AI/ML Role

🚨 Stop Saving Random AI Tools. Start Understanding the Stack.
Most AI projects don’t fail because of the 
model
.
They fail because everything around the model breaks.
And this visual nails that reality 👇
It shows what it actually takes to build production-grade AI systems in Python not just notebooks and demos.

🧠 Python Tools Powering Real AI Systems
🔹 Data Processing & Management
NumPy · Pandas · Polars · Dask
→ Where raw data becomes usable intelligence

🔹 Machine Learning Frameworks
Scikit-learn · XGBoost · LightGBM · CatBoost
→ Structured ML that still runs most businesses

🔹 Deep Learning
PyTorch · TensorFlow · Keras · JAX
→ Vision, NLP, multimodal, foundation models

🔹 MLOps & Automation
Airflow · Prefect · Dagster · Kubeflow
→ Pipelines, scheduling, reliability

🔹 Model Deployment & Serving
FastAPI · BentoML · Streamlit · Gradio
→ From model → usable product

🔹 Experiment Tracking & Monitoring
MLflow · Weights & Biases · Neptune · Comet
→ Reproducibility, metrics, drift tracking

🔹 Data Validation & Evaluation
Great Expectations · Evidently AI · Deepchecks
→ Trust, quality, and correctness

🔹 Visualization
Matplotlib · Seaborn · Plotly · Altair
→ Insights people can actually understand

🔹 Security & Privacy
OpenMined · PySyft · Presidio
→ Compliance, PII protection, safe AI

💡 Big takeaway:
AI today is not “learn a model and ship.”
It’s engineering + orchestration + monitoring + governance.
If you’re a Data Scientist, ML Engineer, or AI Founder, this is the kind of mental map you 
must have to design systems that survive production.

👏 Credits & Appreciation
Visual created by Legend Brij kishore Pandey phenomenal work simplifying a very complex ecosystem.

🔖 Save♻️ Repost for your AI-building friends.

🚨 STOP building “basic RAG.”
That architecture is already outdated.

Most teams are still stuck at:
Split → Embed → Retrieve → Generate
But production AI in 2026 looks very different.
So I created a 25 RAG Architectures Cheat Sheet that maps how real systems are being built today not demo apps, but enterprise-grade AI.

📘 Inside this guide:
• Adaptive RAG for confidence-driven refinement
• Hybrid RAG (Dense + Sparse search)
• Graph-based RAG for relationship reasoning
• Multi-modal RAG (text + image + audio)
• Time-aware & Continual-learning RAG
• Cost-constrained RAG for startups
• Adversarial & Constitutional RAG for safety
• Federated RAG for privacy-preserving systems
• Tiny RAG for edge & mobile devices
• Retry / Resilient RAG for mission-critical apps

…and 15+ more architectures, each with:
✔️ What it is
✔️ When to use it
✔️ Workflow breakdown

💡 Key realization:
RAG is not a feature.
It’s a system design decision.
The difference between:
❌ hallucinating bots
✅ reliable AI products
…is architecture, not prompts.

If you’re:
• Building AI products
• Designing GenAI systems
• Preparing for AI interviews
• Moving from demos → production

Join the learning community -https://lnkd.in/gyhqiuPG

👉 Save this. Revisit it. Build from it.
🔁Repost: Most people know what RAG is very few know which RAG architecture to use and when. This cheat sheet fills that gap.

25 RAG Architectures Cheat Sheet

🚨 Most people are learning AI the wrong way.
And by 2026, that mistake will cost careers.

Right now, “learning AI” is being marketed as:
 Prompt hacks
One-click tools
Fancy demos
But your job won’t be replaced by AI.

It’ll be replaced by someone who understands AI as a system, not a shortcut.
Here’s what actually matters in 2026 
AI is a full-stack skill.

Not a feature. Not a tool. An operating layer for work.
The professionals who win will know how to:
🔹 Think with LLMs
→ prompt structure, reasoning patterns, constraints, output formats
🔹 Ground AI in real data
→ RAG systems, embeddings, vector databases, retrieval strategies
🔹 Build agents that act
→ tool calling, memory, planning, multi-step execution
🔹 Ship real AI products
→ FastAPI, React, vector DBs, Docker, monitoring
🔹 Integrate AI into workflows
→ APIs, webhooks, automation, legacy systems

This is why prompt engineering alone is not enough.
It’s the entry point, not the destination.

The real skill is knowing what to build next:
Prompting → Python → RAG → Agents → Full-stack AI apps
No over-engineering.
No tool chasing.
No waiting to “feel ready.”

💡 The biggest myth right now:
“AI will take jobs.”
The truth:
AI multiplies people who know how to build with it.
Everyone else gets stuck watching.
I created this AI Skills Roadmap for 2026 to cut through the noise and show what actually compounds over time.

If you’re serious about staying relevant:
✅ Save this
✅ Build one real project
✅ Stop consuming, start shipping
🔁 Repost this if you believe AI literacy will be as basic as computer literacy once was.

#AI2026 #GenAI #AIEngineering #FutureOfWork #BuildInPublic #CareerGrowth #Reshmawithai

Skillset guide

Everyone calls it luck

But luck doesn’t show up at 2:00 AM
Luck doesn’t solve problems after a long day.
Luck doesn’t come back tomorrow when motivation disappears.

Discipline does.

Edward Nathan from IIT Hyderabad didn’t land a ₹2.5 crore package overnight.
He earned it quietly by showing up every single day.

For years, he solved at least one coding problem daily.
Not when it felt exciting.
Not when it was convenient.
But even on days when stopping felt easier than continuing.

Those green GitHub squares people admire?
They’re not a measure of brilliance.
They’re a record of consistency.

While many waited for the perfect moment, he built momentum through small, repeatable wins.

One problem a day.
One habit.
Thousands of hours later, the outcome looked like “luck”.

Here’s the part most people miss:
Success is rarely loud while it’s being built.

Late nights compound.
Boring days stack.
Systems outperform motivation.

The offer letter is just a byproduct.
The real achievement is becoming someone who shows up even when no one is watching.

If this resonated, pause and ask yourself:
What’s the one habit you’ll protect this year, no matter how busy life gets?


🔁 Repost : Let’s get motivated not by results, but by the habits that create them. If this inspires you, you’re not alone.

AI PM ≠ ML Engineer. And That’s a Good Thing.
If you think you need to learn PyTorch, deep math, or model training to work in AI,  you’re already on the wrong path.
That belief stops great product thinkers from entering AI.
Let’s kill the myth 👇

Most people assume AI Product Managers must:
1. Train models
2. Write ML pipelines
3. Understand advanced math

Truth:
AI Product Managers don’t build models.
They build clarity, direction, and business value.
Here’s the real-world roadmap to become an AI Product Manager
(no theory fluff, only what works) 

🔹 1. Nail product fundamentals
User problems > features
PRDs, roadmaps, OKRs, success metrics are non-negotiable
 AI won’t fix weak product thinking.

🔹 2. Learn AI at PM depth
You should understand:
• What ML & LLMs can and can’t do
• Why hallucinations happen
• How data quality shapes outputs
No model building required.

🔹 3. Think in data, always
No data = no AI product.
Ask early:
• What data do we need?
• Do we have access?
• Can we trust it?

🔹 4. Understand the AI product lifecycle
Problem → Data → Model → Evaluate → Deploy → Monitor
AI products don’t “ship once.”
They learn, drift, and evolve.

🔹 5. Get comfortable with GenAI
Know the basics:
• Prompts & context
• RAG
• Latency vs cost tradeoffs
• Safety & guardrails
Modern PMs are GenAI PMs.

🔹 6. Measure what matters
Product metrics:
DAU, retention, conversion
AI metrics:
Accuracy, hallucinations, latency, cost
 If you can’t measure it, you can’t manage it.

🔹 7. Practice beats theory
Build case studies
Ship small AI features
Iterate with real feedback
That’s how credibility is built.

Good news:
You don’t need a PhD.
You don’t need advanced math.

You do need:
✔️ Clear thinking
✔️ Curiosity
✔️ Strong product judgment
If you’re a PM, engineer, analyst, MBA, founder, or career switcher, this path is open.

📌 Save this if AI PM roles are on your radar & Follow #reshmawithai| Reshma S
🔁 Repost it because AI PM ≠ model building (and this myth needs to die)
💬 Connect & Comment “AI PM” and I’ll share a step-by-step guide into your Dm's

Join the learning community - https://lnkd.in/gyhqiuPG

Ai Product Manager

I don’t build chatbots.
I ship agents that actually do the work.
Agents in.
Busywork out.
Over the last few months, I’ve been using a very specific AI Agent Blueprint  the same thinking behind the Ultimate MCP Guide to move teams from AI demos to AI that clocks in.
If you’re walking into your first “AI strategy” meeting, read this first.
Here’s what building agents in the real world actually looks like 👇

☑️ Cursor for agentic coding
An editor that understands your entire codebase.
Tab through refactors.
Let it run autonomously when the direction is clear.

☑️ Claude Code in the terminal
No IDE dependency.
Edits files. Runs commands. Thinks across the repo.
Serious leverage for engineers who ship.

☑️ Lovable → prompt to production
React + Tailwind + Supabase out of the box.
You ship a working MVP while others are still debating wireframes.

☑️ Manus AI for autonomous execution
Research. Analysis. Code. Delivery.
It plans and executes while you sleep.

☑️ n8n as the orchestration spine
CRMs. Inboxes. Files. Calendars. Webhooks.
The glue that keeps agents alive at 3am without breaking.
The output is not a paragraph.
It’s a changed system of record.
That means guardrails + observability by default:
• rate limits
• validation
• retries
• human-in-the-loop where it matters
Every action logged.
Every step testable.
No black boxes. No magic.

Where this actually pays off 
→ Intake & scheduling in minutes, not days
→ Document triage with citations
→ CRM hygiene that finally stays clean
→ Support teams with faster first response & tighter SLAs
Agents don’t sit next to the workflow.
They are the workflow.
If your “AI” can’t click buttons, call tools, and update systems 
you don’t have AI.
You have a demo.
If you lead an SMB or mid-market team and want an agent that clocks in on Day 1, I’ll show you what this looks like on your stack.

👇 Comment “AI AGENT”
I’ll share the exact blueprint I use to go from
idea → instrumented agent → measurable ROI.

P.S. What’s the ugliest manual process your team is still stuck with?
♻️ Repost if you’re done with demos.
🔖 Save this if you’re building for production.
Follow Reshma S | #ReshmaWithAI for daily, practical AI systems.

AI AGENT BOOK

🚨 If your RAG is “working” but not reliable this is why.
Most teams are debugging models.
The real bug is chunking.
Chunking isn’t a preprocessing step.

It’s not “split every N characters and add overlap.”
Chunking is retrieval design.
It decides what your system can retrieve…
and what it will never see.

When chunking is wrong, the symptoms look familiar:
• Retrieval feels close, but never precise
• Citations exist, but can’t be trusted
• Answers sound confident, yet miss exact lines
• Token usage keeps climbing
• Accuracy plateaus no matter how many models you try
This is why many RAG systems sound right
but fail in production.

When chunking is done intentionally, everything changes:
• The right evidence shows up consistently
• Answers get shorter, sharper, and grounded
• Hallucinations drop because context is cleaner
• Traceability improves (critical for enterprise & regulated AI)
• Iteration becomes measurable, not guesswork
That’s why I built this carousel.

It breaks down 14 production-grade chunking strategies 
from semantic and structure-aware chunking
to parent–child, query-aware, and evaluation-driven approaches.
No theory.
No “one-size-fits-all.”
Only patterns that actually reduce retrieval failures.

🔖 Save this if you’re building RAG for production
♻️ Repost if your team is shipping customer-facing AI
💬 Comment “CHUNKING” if you want a deep dive on evaluation metrics next

Chunking_Guide

🚀 Launching a Learning & Upskilling Community for AI, ML & Automation
After weeks of celebration, conversations, travel, and deep discussions with students and working professionals, one pattern kept repeating.
People are motivated.
People are capable.
But most are learning in isolation, without direction or real feedback.
My DMs are filled with questions like:
“What should I learn next?”
“Am I industry-ready?”
“How do I actually get noticed?”
This isn’t a motivation problem.
It’s a structure and access problem.

💡 So I’m building something intentional.
A Learning & Upskilling Community focused on:
AI | ML | Automation | Agentic Systems
No noise.
No random tutorials.
No surface-level learning.
Just clarity, practice, and real-world exposure.

 Why this community exists
To bridge the gap between learning → visibility → opportunity.
AI should be learnable.
Careers should be navigable.
Talent should not stay invisible.

What members will get
→ Exclusive hiring updates
Be the first to know about open roles from my team and trusted industry network
→ Real opportunities, real feedback
Share your work, get structured feedback, and get noticed by people who are hiring
→ Curated learning resources
No fluff. Only what actually moves your skills forward
→ Expert sessions
Live conversations with professionals who are building and hiring in AI
→ Ask questions that matter
Get answers directly from practitioners doing real work
→ Career visibility & profile guidance
LinkedIn profile reviews, positioning tips, and guidance on how to present your skills clearly and confidently

🙌 Who this is for
✔️ Students serious about building AI careers
✔️ Working professionals upskilling or switching domains
✔️ Builders who want depth, not buzzwords
 Not for passive learners
 Not for shortcut seekers

👉 Interested in joining?
Fill out this form to get early access:
🔗 https://lnkd.in/grfxT_73
This will be focused, structured, and intentionally small.
Let’s build skills that actually matter.
Let’s make AI learning accessible the right way.

🔖 Save this post
♻️ Repost to help someone turn learning into real opportunity
 Follow Reshma S | #ReshmaWithAI for more Ai insights

Most people think the future of AI is bigger models
That’s already outdated.

2026 won’t be about bigger models.
✅ It will be about smarter, smaller ones.
Welcome to the rise of SLMs, Small Language Models.
While headlines obsess over trillion-parameter LLMs, real-world systems are quietly moving in the opposite direction.
Why?
Because scale in 2026 is not about size.
It’s about cost, speed, and control.

🔍 The Shift Nobody Talks About
LLMs vs SLMs
• LLMs → massive clusters, hundreds of billions of parameters
• SLMs → single GPU (or even CPU), 2B–10B parameters
• LLMs → $5–$60 per million tokens
• SLMs → $0.05–$0.50 per million tokens

And here’s the surprise 👇
On well-defined tasks, SLMs can match or outperform larger models.
🎯 Why Smaller Models Win
Because SLMs are built for focus, not general knowledge.
They shine in:
• Customer support automation
• Product tagging & classification
• Code generation
• Logic & rule-based tasks
• Domain-specific workflows
This is where most production AI actually lives.

 What Made SLMs Powerful?
Three breakthroughs changed the game:
1️⃣ Quantization
FP32 → FP16 → INT8 → INT4
→ 4–8× memory reduction
→ Faster inference, minimal accuracy loss

2️⃣ LoRA Fine-Tuning
Freeze the base model
Train tiny low-rank adapters
→ One model, many tasks, low cost

3️⃣ Flash Attention
Smarter attention computation
Better GPU memory usage
→ Long context, even on small models
Result?
Local. Cheap. Fast. Powerful.

🔮 What This Means for 2026
LLMs won’t disappear.
They’ll become the general intelligence layer.
But SLMs will run the show inside real products.
The winning architecture:
• LLM → broad reasoning
• SLMs → task execution
• LoRA → customization
• Quantization → edge + cost control
This is how companies scale AI
without scaling cloud bills.

📄 PDF Credits: Analytics Vidhya
If you’re building AI systems in 2026,
learning SLMs is no longer optional.
♻️ Repost if you believe smaller models will beat bigger ones.
➕ Follow for more content that makes complex AI feel simple.

Analytics Vidhya

SLM vs LLM

I just finished building a 35+ page, end-to-end guide on LLM Fine-Tuning and it’s finally ready.

After months of research, experiments, failed runs, debugging sessions, and real breakthroughs, I’m excited to share my most comprehensive work so far:
📘 LLM Fine-Tuning Techniques —> With Real Code, Math & Case Studies
This is not a surface-level overview.
It’s a practical handbook designed for people who actually build with LLMs:

✔ ML Engineers
✔ Data Scientists
✔ AI Researchers
✔ GenAI Builders
✔ Anyone serious about mastering LLM fine-tuning

📖 What’s Inside the Guide
Core Fine-Tuning Techniques
Full fine-tuning vs parameter-efficient approaches
PEFT methods explained clearly
LoRA & QLoRA (with math intuition + derivations)
Instruction tuning pipelines
RLHF (reward models, PPO, and training flow)

💻 Hands-On, Production-Focused Code
BERT sentiment fine-tuning (step-by-step)
LoRA with Hugging Face
QLoRA using 4-bit quantization
Instruction dataset formatting best practices
Memory-efficient training and scaling tricks

🏢 Real-World Case Studies
Healthcare → Clinical summarization (92% accuracy)
Legal → Contract risk extraction
Finance → Fraud detection saving ~$50M
E-commerce → 3× faster product description generation

📊 Advanced Concepts
Hyperparameter search strategies that actually work
Evaluation beyond accuracy (what to measure, and why)
Continual & multi-task learning
Deployment best practices
What’s next: MoE, Constitutional AI, and beyond

⚡ Why This Matters
Fine-tuning is no longer optional.
It’s how organizations turn foundation models into domain-specific, high-ROI systems.
This guide is my attempt to make that journey clearer, more practical, and more accessible for engineers and builders working with LLMs.

👍 Follow Reshma S & #ReshmawithAI for AI • ML • GenAI • Agentic AI projects
💬 If you’re working on fine-tuning, LoRA, QLoRA, or GenAI projects, comment “resource”  I’ll share additional notebooks, datasets, and learning material.
🔁 Repost to help AI learners understand LLM fine-tuning with real examples and case studies.
Join the learning community 👉 https://lnkd.in/gyhqiuPG

LLM Fine-Tuning Techniques

I Kept Posting Even When No One Was Watching
20 days.
5,000 new people.

That number looks exciting now.
But it didn’t feel exciting on Day 1.
Day 1 felt like posting into silence.
Day 5 felt like questioning if this was even worth it.
Day 10 felt uncomfortable showing up without knowing if it would work.
But I showed up anyway.

I posted when I was tired.
I shared when it wasn’t perfect.
I chose progress over polish.
I learned in public.
I built in public.
This growth didn’t happen by luck.
It happened because I refused to disappear when no one was clapping.

And here’s the truth I’ve learned the hard way 👇
In 2026, self-branding is no longer optional.
It’s mandatory.
If you don’t show your work,
people won’t know your value.
If you stay silent,
opportunities quietly move on.

Consistency isn’t loud.
It’s boring.
It’s repetitive.
And it’s powerful.

So I’m committing to this publicly:
 More videos (even when it’s uncomfortable)
 More real insights (not recycled noise)
 More behind-the-scenes building
No matter the reach.
No matter the algorithm.
No matter the noise.

Because consistency is the only real key.
And this journey would not feel the same without the people who stood beside me when the results weren’t visible yet.

A heartfelt thank you to my friends and fellow LinkedIn creators who supported, encouraged, and pushed me to keep showing up:
• Brij kishore Pandey  Palanisamy Charlie  Pooja Jain Shushant Lakhyani Manthan Patel Alex Hina Arora  Sangeetha B Naresh Edagotti Chandra Sekhar Saurabh Dubey Gitanjali 
Your likes, comments, reposts, honest feedback, and quiet DMs mattered more than you know 🤍

This growth isn’t just about numbers.
It’s about community, consistency, and collective energy.
We’re just getting started.

Pick one niche, one insight a day, and post consistently.
 Comment "Strategy" I'll send to your Dm's, Self-branding works when you stay visible, not when you wait.

💾 Save this if you’re building quietly
♻️ Repost to remind someone to keep showing up
👉 Follow for what’s coming next

📌 One Sketch That Explains More Than 50 Blogs

Sometimes a simple sketch explains what 50 blogs cannot.
The AI landscape today feels noisy  new terms, new models, new hype every week.

But when you step back, the journey is actually very logical.
This visual captures that journey beautifully.
Huge credit to my legend friend Brij kishore  not everyone can draw and explain AI evolution with this level of clarity. This is hard to do, and it’s done exceptionally well here.

Let me tell the story this sketch shows 👇
Classical AI ->Rules
We started by telling machines exactly what to do.
“If this happens, then do that.”
Powerful for logic, but limited to what humans could explicitly define.

Machine Learning -> Learning from Data
Instead of rules, we gave machines examples.
They learned patterns from data classification, regression, prediction.

Neural Networks -> Inspired by the Brain
We modeled neurons, weights, and learning through feedback.
Machines began learning representations, not just outcomes.

Deep Learning -> Patterns at Scale
With depth and compute, models learned vision, speech, and language.
This is where scale unlocked real-world intelligence.

Generative AI -> Creation
AI moved from understanding to creating.
Text, images, code, audio not copied, but generated from learned patterns.

Agentic AI -> Action
This is the next layer.
AI that can plan, remember, use tools, and take action.
Not just responding, but executing with intent.

The most important insight?
Each layer doesn’t replace the previous one.
It builds on it.
Rules → Learning → Patterns → Creation → Action.
Simple sketch.
Deep understanding.
Sometimes, clarity is the real innovation.

🔁 Repost :Sometimes a simple sketch explains what 50 blogs cannot.
💬 Which layer do you think most teams underestimate today?

AI Architect | AI Engineer | Generative AI | Agentic AI | Tech, Data & AI Content Creator | 1M+ followers

1K followers in 48 hours, I didn’t go viral. I went intentional and gained.
No paid tools.
No viral hacks.
No copying content.
Just understanding LinkedIn + consistent action.
If I can do this, anyone can including you.

 What Most People Get Wrong on LinkedIn
❌ Posting the same way from Day 1
❌ Copy-pasting trending content
❌ Chasing likes instead of attention
❌ Depending on tools instead of strategy
LinkedIn doesn’t reward noise.
It rewards originality, clarity, and consumption.

📈 What Actually Moves the Algorithm
1️⃣ Test, don’t assume
Rotate formats: text, visuals, carousels.
Your audience decides what works, not trends.

2️⃣ Your voice > viral templates
Take inspiration, never duplication.
Copied content = suppressed reach.

3️⃣ Design matters more than you think
Clean visuals = longer reading time.
Low effort design = scroll away.

4️⃣ Dwell time is king 
If people stop, read, and finish your post,
LinkedIn pushes it further.

5️⃣ Consistency beats intensity
2–3 high-quality posts per week
outperform random daily posting.

 Hard Truth (But Necessary)
Self-branding is not optional anymore.
If you don’t tell your story:
• Someone else will
• Or you’ll stay invisible
Opportunities don’t just come from skills 
they come from visibility + trust.

You don’t need:
• Perfect English
• A big audience
• Fancy tools
You just need:
✅ To start small
✅ To show up consistently
✅ To speak from experience
I started.
It worked.
Now it’s your turn.

🔁 Repost for early access to proven LinkedIn growth strategies.
💬 Connect & Comment “STRATEGY”
I’ll share the exact posting strategy that helped me grow.
👉 Follow Reshma S | #reshmawithai for real LinkedIn growth & personal branding insights


Join the learning community: https://lnkd.in/gjDjHn95 to get started

AI Engineer | 1M+ Impressions | Helping Students professional to Build Next-Gen AI Agents | Mentor Empowering Students & IT Pros | Passionate About Smart, Scalable Systems

🚫 Fine-tuning isn’t dead.
Bad fine-tuning is.
Most teams still fine-tune LLMs like it’s 2022.
Random datasets.
One-shot training.
No evals.
No production feedback.
And then they wonder why models fail in real workflows.

So I put together this carousel:
13 LLM fine-tuning techniques that actually work in production.
Not theory.
Not benchmarks-only advice.
Only methods we’ve seen improve reliability, tool-use, and real-world behavior.
Inside this carousel, you’ll find:
• Domain & task-adaptive pretraining (DAPT, TAPT)
• Multi-stage curriculum fine-tuning
• QLoRA done right (not defaults)
• DORA / AdaLoRA for adaptive capacity
• Long-context tuning that avoids “lost-in-the-middle”
• RAG-aware fine-tuning with citation discipline
• Preference tuning (DPO, IPO, KTO)
• Tool-use fine-tuning with execution feedback
• And the eval mindset most teams still ignore

👉 Key takeaway:
Fine-tuning that ships = training + ruthless evaluation.
If you’re building LLMs for real users not demos this matters.
🔖 Save this for later
♻️ Repost if you’re building production AI
💬 Comment “FT” if you want a deeper breakdown of any technique

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