HemansAI

Data & AI Consulting, Development, AI Leadership Training.

Founder

85% of AI products and projects fail.

The two biggest challenges businesses face with AI:

1. Estimating and demonstrating AI value [VPs, CEOs, product managers, and decision-makers]
2. Lack of talent and skills [AI engineers and data professionals]

Most business leaders fall into the first category.

Even if you’re skilled at your job, your team might lack confidence that the AI solution will deliver the desired outcomes.

It’s frustrating, right? You see others succeeding with AI, but your team your team is stuck planning, fixing, or juggling disconnected tools.

AI is supposed to bring automation, sharper insights, and smarter decisions.

But when you try to implement it, you encounter chaos:

- Lack of clarity on ROI
- Endless troubleshooting instead of progress estimation
- Too many disconnected systems to demonstrate AI value

It’s easy to wonder if AI will ever deliver what it promises for your business and career.

Now, imagine a future where AI doesn’t just work—it works seamlessly in your organization.

Your data isn’t just stored—it powers real-time insights, precise forecasts, and simplified processes.

Picture an AI strategy that works without constant troubleshooting—a setup that scales and brings results.

I’m an AI consultant, here’s how I can help you:

- the AI leadership coaching

It’s about creating an AI path that’s right for your business or your career with real results, not just ideas.

Let’s connect. we’ll talk about AI strategies, products, and career opportunities.
connect@himanshuramchandani.co

Himanshu Ramchandani

ChatGPT can't do math. So why does your agent get calculations right?

You built an agent that "reasons"

Added ChatGPT + some tools

Called it intelligent

But here's what's actually happening:

↳ user asks a question
↳ LLM extracts parameters
↳ function gets called
↳ result comes back

That's it.

No magic. No intelligence. Just orchestration.

The real agent architecture:

Orchestration Layer → manages memory, state, and planning
Model → understands natural language intent
Tools → executes actual capabilities

When you ask "what's 65 × 85?"

The LLM doesn't calculate.

It CAN'T calculate.

It recognizes a math pattern, extracts numbers, calls calculator tool.

This is why your agent hallucinates prices but gets calculations right.

One uses training data (unreliable)

Other uses tools (deterministic)


Before: "my agent is smart"
After: "my agent routes to the right tool"

Stop building smarter prompts.

Start building better tool architectures.

Follow Himanshu Ramchandani for real AI engineering.


P.S. Building 7 production AI Agents in my bootcamp. Link in comments.
Join 75 Hard AI Engineering Challenge
Join 30 Day AI Agent Challenge [starting 2nd December]

AI Engineering & Leadership • Building an AI product studio and providing Development & Consulting services

Your transformer knowledge is worthless

- you read the "Attention is All You Need" paper
- you took a Hugging Face course
- you fine-tuned BERT

Congratulations - you are still useless

Here's the truth nobody tells you:

90% of AI engineers can't explain what happens in model.forward()

↳ they just stack layers and pray
↳ when training fails? chatGPT or stack overflow
↳ when memory explodes? smaller batch size
↳ when inference is slow? "just add more GPUs"

This is why you'll never build anything real

The engineers building GPT, Claude, and Llama?

They don't just use transformers

↳ they optimize every matrix multiplication
↳ they understand gradient flow at scale
↳ they know exactly why their model fails

Stop collecting certificates

Start understanding fundamentals

- matrix multiplication > another course
- calculus > another tutorial
- production constraints > another paper

You want to be valuable?

Build a transformer from scratch

Optimize it until it's 5x faster

Deploy it at scale

Then you're an AI engineer

Until then? You're just another API caller

Follow Himanshu Ramchandani for the brutal truths about AI engineering

P.S. - Get the deep dive in the FREE 75 Hard AI Engineering Challenge. AI Agents Bootcamp, ending early bird access today. (last 6 early bird seats left).

You're building AI agents. But you don't understand how they scale - Here's Why:

Every agent you deploy uses resources

Every API call costs money

Every response consumes compute

Most engineers build agents without understanding efficiency

Here's what changes everything:

- when you see this: agent.run()
- that innocent line is burning through your compute budget

Scaling laws predict exactly how your agent will perform:

↳ model size vs accuracy
↳ token usage vs cost
↳ response time vs user satisfaction

The truth about production agents:

Before understanding scaling:

- random model choices
- unpredictable costs
- can't forecast performance

After understanding scaling:

- calculate costs before deploying
- choose optimal model sizes
- predict agent behavior at scale

Chinchilla's insight: smaller models + more data = better results + lower costs

A 7B model trained right beats a 13B model trained poorly

This is Day 26 in the 75 Hard AI Engineering Challenge [join for free]

Real engineering.

Real results.


Follow me Himanshu Ramchandani for daily AI engineering insights

PS: FREE AI Agents Workshop this weekend. Join via newsletter 🚀 - AI Agent Bootcamp spots filling fast (check comments)

Comfort is why most AI engineers stay mediocre

Day 30 of the 75 Hard AI Engineering Challenge is done

Half the cohort is thriving

The other half? Drowning in content they have never seen before

"Himanshu, this is too hard"
"Can we slow down?"
"I don't understand derivatives"

My response?

(this dialogue from KungFuPanda movie)

The engineers building production AI systems aren't comfortable

- they are learning RAG at midnight
- debugging embeddings at 5 AM
- implementing attention from scratch on weekends

discomfort = growth

Here's what I'm seeing from those who push through:

↳ week 1: confused
↳ week 2: frustrated  
↳ week 3: connecting dots
↳ week 4: building confidently

The struggle isn't a bug <> It's the feature

Real AI engineering isn't copying tutorials

It's understanding

- why your training explodes
- why your embeddings cluster weird
- why your inference is slow

45 days to go. The best part?

You're already different than Day 1.

Still accepting latecomers who want the challenge

Follow Himanshu Ramchandani for real AI engineering

Here are some updates:
↳ 75 Hard AI Engineering Challenge ($0) 
↳ AI Agents Bootcamp (Start at $0) - add 7 deployed Agents in your portfolio
↳ 30 Day AI Agents Challenge ($0) starting 2nd December
↳ AI Builder's Bootcamp (Launching in January 2026)
↳ 4 Hands-On Workshop(for professionals)/Webinar(for leaders) in November on AI Agents 
↳ Product Launch - Knowledge Engine at Dextar
↳ AI Leadership Accelerator - The Elite - beta version (DM for pre booking)
↳ Long form AI Engineering deep dives on substack (sharing this week)
↳ Dropping new episode of Humanly Foolish podcast (Ask Anything AI)

- more updates will share later


P.S. - Free AI Agents Workshop this weekend. Link in comments.

You are spending $1,500 extra on API calls. Because you are still using JSON

Every API call you make:

- chatGPT charges per token
- claude charges per token
- gemini charges per token

That employee dataset? 126,860 tokens in JSON

Same data in TOON? 49,831 tokens

TOON (Token-Oriented Object Notation)

↳ 60% fewer tokens
↳ 60% lower costs
↳ same information

Here's what nobody tells you:

JSON wasn't built for LLMs.

It's verbose by design

JSON:

{
"users":[{"id":1,"name":"Alice"},
                {"id":2,"name":"Bob"}]
}
```

TOON speaks LLM's language:

users[2]{id,name}:
 1,Alice
 2,Bob


but Himanshu, why this matters?

When you send data to GPT-4:

↳ repeating field names = wasted tokens
↳ extra brackets = wasted tokens
↳ redundant syntax = wasted tokens

TOON declares structure once

Streams data as rows

Real impact at scale:

↳ 1M API calls with that employee data?

JSON: $2,537 in tokens
TOON: $996 in tokens

You just saved $1,541

What it means-

Before: blindly serialize everything as JSON
After: encode as TOON for LLM input, get 30-60% token reduction

Have you calculated your token costs? [repo link in comments]

Follow me Himanshu Ramchandani for practical AI engineering insights

P.S. - AI Agents bootcamp starting on 2nd December, 23 out of 60 are taken, today is last day of early bird access.

You are running local (on-premises) LLMs but don't know if they are actually good

- every AI engineer downloads Llama or Mistral
- runs them locally
- feels excited about - free inference

Then reality hits:

- is this output even correct?
- is GPT-4 actually better?
- am I just burning electricity for worse results?

Here's what nobody tells you:

Running local models without benchmarking is like driving blindfolded

You need three numbers:

↳ speed (tokens/second)
↳ quality (% correctness)
↳ cost (real cost per 1M tokens)

When I started the 75 Hard AI Engineering Challenge,
I thought local = always better

I was wrong

Real example from Day 32:

- mistral 7B locally: 35 tok/s, $1.50 per 1M tokens (estimated)
- gpt-4 API: 18 tok/s, $30,000 per 1M tokens (claimed)

For code generation?

- mistral: 67% correct
- gpt-4: 100% correct

The question is not "which is faster"
The question is "which solves MY problem"

For 100K tokens/month → use APIs
For 10M tokens/month → local wins
For sensitive data → local (on-premises) only

benchmarking:

Before:

- "this model feels slow"
- "the output seems wrong"
- "maybe I should try GPT-4"

After:

- "this model runs at 32 tok/s"
- "it's 89% accurate on my test set"
- "break-even is at 2M tokens/month"


Do this:

↳ pick 100 real examples from your work
↳ run through 3 different models
↳ measure speed, cost, quality
↳ choose based on data, not hype


Follow me Himanshu Ramchandani for daily AI engineering insights that actually matter

PS: Join the FREE 75 Hard AI Engineering Challenge [link in comments]

PPS: Learn AI Agents in the upcoming bootcamp, limited seats, FREE workshop on RAG  Agents today 7:30 PM IST

If you want to ship a production LLM app by 2025, you have less than 40 days so here's what you are going to do:

Stop calling OpenAI and praying it works

Your app breaks when their API goes down

You have zero control

Here's what production engineers actually do:

Build fallbacks

↳ openAI down? switch to anthropic
↳ both down? use your self-hosted model
↳ all automatic - users never notice

The math that changes everything:

- commercial API only:
$9,500/month at 50M requests

- hybrid approach (API + self-hosted):
$730/month at 50M requests

That's 92% savings

Companies like Notion and Shopify don't use one LLM

They use smart routing between multiple models

This is part of the Day 35 of the 75 Hard AI Engineering Challenge:

↳ multi-provider fallback system
↳ self-hosted model with vLLM
↳ cost optimization logic
↳ production deployment on AWS

This isn't theory

The difference between a demo and a product? 

reliability when things break

Build something that actually ships.


Follow Himanshu Ramchandani for real AI engineering.


PS: Build a fridge AI agent that will order groceries automatically, join the community and bootcamp waitlist.

Stop building smarter agents. Start building agent teams.

Everyone's obsessing over:

- better prompts
- bigger context windows
- smarter models

the real breakthrough is multi-agent architecture

Single Agent Problem:

You: optimize my resume for this role
Agent tries to:

- understand job requirements
- analyze your background
- rewrite experience
- create cover letter
- format everything

It's doing 5 jobs at once

Quality suffers

Multi-agent solution, same task, different approach:

Agent 1 → deep dives on company (research specialist)
Agent 2 → rewrites your resume (writing specialist)
Agent 3 → creates cover letter (personalization specialist)

Each agent is great at ONE thing.

- they share context
- collaborate
- roduce better outputs

Difference:

- single agent: Jack of all trades, master of none
- multi-agent: Specialists working together


Architecture Basics:

Orchestration layer → Routes tasks to right agents
Shared memory → Agents access same context
Sequential workflow → Output of one feeds into next
Parallel processing → Multiple agents work simultaneously

CrewAI, LangGraph, AutoGen already enable this.

Tonight at 7:30 PM IST, I'm showing you how.

We build a multi-agent system that:

→ researches companies
→ optimizes your resume
→ writes personalized cover letters

Live and step by step

Free workshop [Link in comments]

This is how production AI systems actually work

Follow Himanshu Ramchandani for real AI engineering

You built an AI agent from scratch yesterday

Most people think it's complex - It's not

Here's what I showed 87 engineers:

- AI agents aren't magic
- they are just 3 components working together
- anyone with basic Python can build one


We built an agent that:

↳ understands natural language
↳ uses external tools (calculator, web search)
↳ makes decisions autonomously
↳ handles real-time queries

Before: "AI agents are too complex for me"
After: "I just built one in 90 minutes"


Here's the truth:

- you don't need a PhD
- you don't need years of experience
- you need the right framework

We went from theory >> working code in one session

Most engineers overthink this


AI Agents Bootcamp starts Dec 2nd

↳ 7 production agents in 4 weeks
↳ Live builds, not tutorials
↳ Early bird closes in 24h

ROI - Build what companies pay $150K/year engineers to create

Thank you for joining the workshop. 7 more are coming in November.


Want the AI Agents Resource Bundle? Comment "AGENT" 👇

Follow Himanshu Ramchandani for practical AI engineering.

PS: Hosting 60 min FREE Webinar on - AI Agents for Business Leaders: ROI & Implementation Strategy, 9th November 9 AM IST [Register Comments]

🎤 Speaker Spotlight
🚀 AI Unplugged: A Prelude to DevFest 2025
Get ready to learn from the best!
Join Himanshu Ramchandani, expert in AI Engineering & Leadership, as he shares insights on
💡 “How to Learn AI Fast with a Builder’s Mindset?”

Discover how to accelerate your AI journey by thinking like a creator, innovator, and problem-solver.

📅 Date: Saturday, 15th November 2025
🕙 Time: 10:00 AM onwards
📍 Venue: Devi Ahilya University Auditorium, Indore
🔗 Register here: https://lnkd.in/dufGG_4r

#SpeakerSpotlight #DevFest2025 #PreDevFest2025 #GDGIndore #AIUnplugged #GoogleDevelopers #LearnWithGDG #BuilderMindset #AI #TechCommunity #IndoreTech #DeveloperCommunity

I've never shared this before

6 months ago, a senior engineer got rejected for promotion

"You use AI tools. We need someone who can architect AI systems."

That's the gap in 2025

Companies don't want LLM users

They want AI Agent Architects

I'm launching the AI Agents Bootcamp December 2nd.

What you build:

↳ 7 production-ready agents in 4 weeks
↳ ReAct patterns & planning systems
↳ LangGraph state machines
↳ Multi-agent orchestration
↳ Production deployment with guardrails

Real results:

Mamta (Amazon): "made my AI engineering transition"
Saravanan: "went from mechanical to AI engineer"
Meena (Amazon): "hands-on approach changed everything"

FREE workshops before launch:

Nov 19: RAG Agents Production
Nov 26: Deploy FastAPI + Docker

189 waiting. 60 seats only.

Two paths:

1 - Join FREE workshops (link in comments) and get FREE AI Agents Guides
2 - Secure bootcamp seat now

Not for quick hacks.

For engineers ready to architect systems companies fight over.

♻️ Repost if you know someone stuck at "just using ChatGPT"

Follow me Himanshu Ramchandani for AI engineering that matters

STOP using LLM APIs like toys

You're calling OpenAI's API like it's a REST endpoint

It's not

Every junior dev I meet:

- hardcodes API keys in Python files
- no retry logic
- no fallback providers
- no token management

Then their app crashes in production and they blame the API

Here's the truth:

↳ they'll fail on you
↳ llm APIs aren't reliable
↳ anthropic will have outages
↳ openAI will hit you with rate limits

Your $4k bill? That's your retry loop gone wrong

But nobody tells you this because they're too busy showing you toy examples from documentation

Production AI engineering isn't about making API calls

It's about:

↳ circuit breakers
↳ cost optimization
↳ exponential backoff
↳ multi-provider fallbacks

Your GPT-4 call doesn't need GPT-4

Use GPT-3.5 and save 80% on costs

Your system crashes?

You didn't classify your errors properly

Stop copying tutorials

Start building systems that don't break when the API sneezes

Follow me Himanshu Ramchandani for production AI that actually works

PS: This is from the Day 30 guide of 75 Hard AI Engineering Challenge, Join the AI Agents Bootcamp is Live.

[Live Workshop] RAG Agents - Build a production-ready document Q&A system


What we are going to cover:
- Retrieval-Augmented Generation
- Embeddings and Vector Representations
- Vector Databases
- RAG Pipeline
- Hands-On Project

Date: 19th November, 2025 (Today)
Time: 7:30 PM IST

→ why vector DBs aren't enough
→ how persistent memory changes everything
→ what enterprise RAG actually needs


Register by joining the workshop inner circle group [link in comments]

[Webinar] AI Agents for Business Leaders - ROI & Implementation Strategy

- roadmap
- case studies
- justifying team investment

Date: 10th November 2025
Time: 7 PM IST

PS: Register for the Webinar for FREE [check comments]

Happiest Minds Technologies

𝐏𝐫𝐨𝐣𝐞𝐜𝐭: 𝐆𝐞𝐧𝐀𝐈 𝐂𝐨-𝐏𝐢𝐥𝐨𝐭 𝐟𝐨𝐫 𝐀𝐮𝐝𝐢𝐭 𝐃𝐨𝐦𝐚𝐢𝐧 𝐐&𝐀'𝐬
𝐑𝐨𝐥𝐞: 𝐆𝐞𝐧𝐀𝐈 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫
As a GenAI Developer, I worked on developing a GenAI Co-Pilot to assist users in the audit process. The primary objective was to provide quick and accurate answers to user queries, reducing manual searching time.

𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 𝐔𝐬𝐞𝐝
- Python
- Azure Open-AI GPT-4 LLM
- LlamaIndex as orchestration framework
- RAG technique for retrieving, augmenting, and generating responses
- Prompt techniques for structured answers and reducing hallucinations
- Streamlit: For interactive User Interface 

𝐈𝐦𝐩𝐚𝐜𝐭
The GenAI Co-Pilot helped users answer their questions, clear doubts, and reduced valuable time wasted in searching for correct answers. We achieved an accuracy of 86%, improved from 75% through optimization and unique implementation techniques.

Senior Data Scientist

Mindshare India

𝐏𝐫𝐨𝐣𝐞𝐜𝐭 – 𝐌𝐚𝐫𝐤𝐞𝐭 𝐌𝐢𝐱 𝐌𝐨𝐝𝐞𝐥𝐥𝐢𝐧𝐠- 𝐌𝐌𝐌			       
• 𝐎𝐛𝐣𝐞𝐜𝐭𝐢𝐯𝐞: Build a MMM model on the advertising spends data using regression for optimizing spends on media 
channels and analyze the impact of macroeconomic and other factors on the sales.
• Performed the data acquisition from GCP and client database, data preprocessing, data validation and 
analysis.
• Applied geometric adstock transformation on the media variables to introduce the advertising properties 
(e.g., decay, lag-lead) into the model. Used Facebook Prophet library to extract monthly 
trends.
• Built nested regression model to encounter issue of collinearity. Predicted store sales with 0.93 R2 score.
• Performed decomposition of the variables to understand the incremental effects of each media spends and 
external variables on sales, visualized using waterfall chart.
• Automated adstock hyperparameter tuning and deployed MMM nested model on local machine using 
streamlit.

Data Scientist

Enrichepro Software Pvt. Ltd. 

𝐏𝐫𝐨𝐣𝐞𝐜𝐭: 𝐉𝐨𝐛 𝐭𝐢𝐭𝐥𝐞 𝐩𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧 (𝐍𝐋𝐏 𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐩𝐫𝐨𝐛𝐥𝐞𝐦)
• Given the features like Job description, domain info, job level, country, and other relevant features, performed NLP and feature engineering to use the text data information in predicting job title.
• Build and compared Logistic regression and tree-based ensemble models, selected XGBoost model which gave strong probability values for class labels. 
• Built end-to-end ML pipeline using MLflow Recipe, for model tracking and registering the validated model.

 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 – 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝘀𝗲𝗴𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝘂𝘀𝗶𝗻𝗴 𝗰𝗹𝘂𝘀𝘁𝗲𝗿𝗶𝗻𝗴 𝗯𝘆 𝗶𝗻𝗰𝗼𝗿𝗽𝗼𝗿𝗮𝘁𝗶𝗻𝗴 𝗖𝗟𝗧𝗩 𝗮𝗻𝗱 𝗥𝗙𝗠 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀.
• 𝐎𝐛𝐣𝐞𝐜𝐭𝐢𝐯𝐞: Categorize the customers into High, Medium and Low value to drive the marketing efforts to maximize retention rate and acquire new customers.
• Performed data preprocessing and feature engineering to extract features like recency, frequency, monetary value (RFM), average order value, CLTV score, etc.
• Applied K-means clustering algorithm on the data with all the necessary features to segment the customers.
• Delivered results using dashboard and performed monitoring and retraining of the model.

ICICI Lombard

𝐏𝐫𝐨𝐣𝐞𝐜𝐭: 𝐅𝐫𝐚𝐮𝐝 𝐜𝐥𝐚𝐢𝐦𝐬 𝐩𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐢𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧 𝐨𝐟 𝐭𝐡𝐞 𝐟𝐥𝐚𝐠𝐠𝐢𝐧𝐠 𝐬𝐲𝐬𝐭𝐞𝐦 𝐢𝐧 𝐭𝐡𝐞 𝐬𝐨𝐟𝐭𝐰𝐚𝐫𝐞.
•	𝐎𝐛𝐣𝐞𝐜𝐭𝐢𝐯𝐞: Classification of the fraudulent claims and flagging them in the initial stages of claim process.
•	Developed understanding of business problem and contributed in designing project stages.
• Performed Data acquisition, preprocessing, EDA, and feature engineering to derive new features from 
existing. Used transfer learning using BERT to extract word vectors for text data i.e., cause of accident.
• Extracted the data using SQL and preprocessed the big data using parallel computing systems like Dask.
• Trained Logistic regression as base model and finalized Random Forest after comparing other models.
• Generalized the model by testing it against the test queries to make the model production ready.
• Contributed in end-to-end deployment in AWS Sagemaker, created endpoint and carried out API testing in Postman.

𝐏𝐫𝐨𝐣𝐞𝐜𝐭: 𝐒𝐞𝐠𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 𝐨𝐟 𝐥𝐨𝐰, 𝐦𝐞𝐝𝐢𝐮𝐦 𝐚𝐧𝐝 𝐡𝐢𝐠𝐡 𝐥𝐢𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐜𝐥𝐚𝐢𝐦𝐬 𝐮𝐬𝐢𝐧𝐠 𝐜𝐥𝐮𝐬𝐭𝐞𝐫𝐢𝐧𝐠.
•	𝐎𝐛𝐣𝐞𝐜𝐭𝐢𝐯𝐞: Segmenting the claims in low, medium and high liability claims to maintain equal workloads amongst surveyors as well as assigning high liability claims to external surveyors.
•	Developed understanding of business problem and performed data acquisition, data preprocessing and EDA.
• Used domain knowledge and feature engineering techniques to derive the new features from existing.
• Used K-means clustering to cluster the claims in low, medium and high liability claims.
• Developed a rule-based system with the help of IT team to maintain the workload within surveyors.

Data Science Analyst (CSM)

HDFC ERGO General Insurance

𝐏𝐫𝐨𝐣𝐞𝐜𝐭: 𝐅𝐫𝐚𝐮𝐝 𝐢𝐧𝐬𝐮𝐫𝐚𝐧𝐜𝐞 𝐜𝐥𝐚𝐢𝐦 𝐩𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐚𝐥𝐞𝐫𝐭𝐢𝐧𝐠.
• 𝐎𝐛𝐣𝐞𝐜𝐭𝐢𝐯𝐞: Classification of the fraudulent claims and integrating it with app that will send notification
alert.
• Performed data extraction from company’s database using MySql, stored and maintained updated data.
• Performed preprocessing like, removing duplicate and missing value rows etc., using Python and Excel.
• Completed basic EDA using Python and Tableau and presented the insights of the data before team.
• Performed other supporting tasks to build and productionize the model.

Claims Analyst (Executive)

-𝐸𝑥𝑝𝑒𝑟𝑖𝑒𝑛𝑐𝑒𝑑 𝐷𝑎𝑡𝑎 𝑆𝑐𝑖𝑒𝑛𝑡𝑖𝑠𝑡 𝑤𝑖𝑡ℎ 𝑏𝑢𝑠𝑖𝑛𝑒𝑠𝑠 𝑎𝑐𝑢𝑚𝑒𝑛. 𝐻𝑒𝑙𝑝𝑖𝑛𝑔 𝑏𝑢𝑠𝑖𝑛𝑒𝑠𝑠𝑒𝑠 𝑡ℎ𝑟𝑖𝑣𝑒 𝑡ℎ𝑟𝑜𝑢𝑔ℎ 𝐷𝑎𝑡𝑎 𝑆𝑐𝑖𝑒𝑛𝑐𝑒 𝑤𝑖𝑡ℎ 𝑝𝑟𝑜𝑣𝑒𝑛 𝑡𝑟𝑎𝑐𝑘 𝑟𝑒𝑐𝑜𝑟𝑑 𝑜𝑓 𝑖𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑛𝑔 𝑎𝑛𝑑 𝑎𝑝𝑝𝑙𝑦𝑖𝑛𝑔 𝐷𝑎𝑡𝑎 𝑆𝑐𝑖𝑒𝑛𝑐𝑒 𝑡𝑒𝑐ℎ𝑛𝑖𝑞𝑢𝑒𝑠 𝑡𝑜 𝐵𝑢𝑠𝑖𝑛𝑒𝑠𝑠 𝑝𝑟𝑜𝑏𝑙𝑒𝑚𝑠.
-𝐸𝑥𝑝𝑒𝑟𝑖𝑒𝑛𝑐𝑒 𝑖𝑛 𝑑𝑒𝑣𝑒𝑙𝑜𝑝𝑚𝑒𝑛𝑡 𝑎𝑛𝑑 𝑑𝑒𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡 𝑜𝑓 𝑀𝑎𝑐ℎ𝑖𝑛𝑒 𝐿𝑒𝑎𝑟𝑛𝑖𝑛𝑔, 𝐷𝑒𝑒𝑝 𝐿𝑒𝑎𝑟𝑛𝑖𝑛𝑔 𝑎𝑛𝑑 𝑀𝑎𝑟𝑘𝑒𝑡 𝑀𝑖𝑥 𝑀𝑜𝑑𝑒𝑙𝑠 𝑢𝑠𝑖𝑛𝑔 𝑃𝑦𝑡ℎ𝑜𝑛 𝑜𝑛 𝑙𝑜𝑐𝑎𝑙 𝑚𝑎𝑐ℎ𝑖𝑛𝑒 𝑎𝑛𝑑 𝑐𝑙𝑜𝑢𝑑 𝑖𝑛𝑠𝑡𝑎𝑛𝑐𝑒𝑠.
-𝐵𝑒𝑙𝑖𝑒𝑣𝑒 𝑖𝑛 𝑡ℎ𝑒 𝑏𝑒𝑎𝑢𝑡𝑦 𝑜𝑓 𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛, 𝑡ℎ𝑒 𝑝𝑜𝑤𝑒𝑟 𝑜𝑓 ℎ𝑎𝑟𝑑 𝑤𝑜𝑟𝑘, 𝑎𝑛𝑑 𝑡ℎ𝑒 𝑛𝑒𝑐𝑒𝑠𝑠𝑖𝑡𝑦 𝑜𝑓 𝑐𝑜𝑚𝑝𝑙𝑒𝑡𝑖𝑜𝑛.
-𝑆𝑝𝑒𝑐𝑖𝑎𝑙𝑡𝑖𝑒𝑠: 𝐼𝑑𝑒𝑎𝑡𝑖𝑜𝑛, 𝑒𝑥𝑒𝑐𝑢𝑡𝑖𝑜𝑛 𝑠𝑡𝑟𝑎𝑡𝑒𝑔𝑦, 𝑎𝑛𝑑 𝑑𝑟𝑖𝑣𝑖𝑛𝑔 𝑎 𝑝𝑟𝑜𝑑𝑢𝑐𝑡 𝑓𝑟𝑜𝑚 𝑠𝑡𝑎𝑟𝑡-𝑡𝑜-𝑒𝑛𝑑.
-𝑅𝑒𝑐𝑒𝑖𝑣𝑒𝑑 𝐴𝑤𝑎𝑟𝑑: 𝐺𝑟𝑜𝑢𝑝𝑀 𝐶ℎ𝑜𝑟𝑒𝑜𝑠'22 𝑎𝑤𝑎𝑟𝑑𝑠- 𝑁𝑒𝑤𝑏𝑖𝑒 𝐼𝑛 𝑇ℎ𝑒 𝑆𝑝𝑜𝑡𝑙𝑖𝑔ℎ𝑡.

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Himanshu Ramchandani

Akshay Bhasme

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The Battle

Himanshu Ramchandani

Akshay Bhasme

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Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Himanshu Ramchandani's LinkedIn Performance

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Performance ScoreHimanshu Ramchandani's LinkedIn Performance