Head of AI/ML (10 years) with a strong background in Machine Learning and MLOps across various domains including information retrieval (Search, Recommendations, RAG-systems & ChatBots), NLP, and Customer Analytics. Brand new: AI Agents!

I specialize in guiding cross-functional teams of ML Engineers, Software Engineers, etc. to build scalable, event-driven ML solutions with a focus on robust MLOps practices using Python/PySpark and cloud platforms like AWS, GCP, etc. - (GCP Certified ML Engineer)

Currently, at HeyJobs, I have been fortunate to initiate and lead multiple complex ML projects (Search, RecSys, NLP and AI Agent) that that have significantly outperformed previous benchmarks and generated substantial business value. Additionally, I also designed and lead the implementation of a new MLOps and monitoring framework that has been embraced across all Data Science teams within the company.

Additionally, I love learning the latest advancements in the ML space, always exploring new ideas to push boundaries and improve outcomes. I’m also dedicated to mentoring and sharing knowledge, focused on building systems that drive innovation and business outcome.

I’m Head of AI/ML with more than 9+ years of experience.
6 pieces of advice I would give to people in their 20s, who want to make a career in AI/ML in 2026:

1️⃣ 𝗠𝗮𝘀𝘁𝗲𝗿 𝘁𝗵𝗲 𝗯𝗼𝗿𝗶𝗻𝗴 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀 𝗯𝗲𝗳𝗼𝗿𝗲 𝘁𝗵𝗲 𝘀𝗵𝗶𝗻𝘆 𝗟𝗟𝗠 𝘁𝗿𝗶𝗰𝗸𝘀
→ Nail linear regression, regularisation, loss functions, TF-IDF & BM25.
→ Explain tokenisation and embeddings from scratch - don’t just import Hugging Face.
→ Build a non-linear model on a toy dataset you created yourself; understand why it works. (yes, it still matters)

2️⃣ 𝗧𝗵𝗶𝗻𝗸 “𝘀𝘆𝘀𝘁𝗲𝗺 𝗳𝗶𝗿𝘀𝘁, 𝗺𝗼𝗱𝗲𝗹 𝘀𝗲𝗰𝗼𝗻𝗱”
→ Sketch an end-to-end pipeline: ingestion → features → model → serving → monitoring.
→ Optimise latency & cost before you celebrate your Accuracy scores.
→ Practise trade-offs: When is a managed LLM API fine? When do you self-host a smaller model?

3️⃣ 𝗚𝗲𝘁 𝗵𝗮𝗻𝗱𝘀-𝗼𝗻 𝘄𝗶𝘁𝗵 𝗠𝗟𝗢𝗽𝘀, 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗻𝗼𝘁𝗲𝗯𝗼𝗼𝗸𝘀
→ Spin up SageMaker or Vertex AI, register a model, deploy an endpoint, add CI/CD in GitHub Actions.
→ Containerise a tiny FastAPI service that serves your model; push it to AWS ECR.
→ Instrument basic monitoring (Grafana/W&B/Kibana) and alert on drift or spikes.

4️⃣ 𝗕𝘂𝗶𝗹𝗱 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻 & 𝗽𝗿𝗼𝗱𝘂𝗰𝘁 𝗶𝗻𝘁𝘂𝗶𝘁𝗶𝗼𝗻
→ Translate “↓ latency by 200 ms” into “checkout conversion ↑ 3 %”.
→ Ask “Why are we solving this?” before “Which model should we try?”.
→ Learn to defend architecture choices to product & infra teams in plain English.

5️⃣ 𝗖𝘂𝗿𝗮𝘁𝗲 𝘆𝗼𝘂𝗿 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗽𝗮𝘁𝗵 - 𝗱𝗲𝗽𝘁𝗵 𝗯𝗲𝗮𝘁𝘀 𝗙𝗢𝗠𝗢
→ Pick one domain (e.g. NLP or CV) and go deep: courses → books → papers → small projects → production clone.
→ Certifications (AWS ML Specialty, etc.) are great frameworks - use them, then go beyond docs and experiment/hands-on.
→ Ignore the noise of “10 agent patterns in a weekend.” Reliable systems are not built overnight and no one knows everything.
→ Start making use of coding assistants

6️⃣ 𝗜𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁 𝗼𝗻𝗲 - 𝗣𝗿𝗼𝘁𝗲𝗰𝘁 𝘆𝗼𝘂𝗿 𝗲𝗻𝗲𝗿𝗴𝘆 - 𝗵𝘆𝗽𝗲 𝗳𝗮𝘁𝗶𝗴𝘂𝗲 𝗶𝘀 𝗿𝗲𝗮𝗹
→ LinkedIn will flaunt “weekend RAGs” and “one-click agents.” Production reality is slower, messier, and far more grounded.
→ Schedule focused blocks, log off social feeds, and take breaks. A rested engineer ships more resilient systems.

PLEASE...check your dearest content creator's profile - 
check if they really have any experience. 
Don't fall for this trap!

--
♻️ Repost to share with your network 
🔔 Follow me, Shantanu for production AI/ML/MLOps & careers
➕ Join 38.000+ AI/ML builders here: https://lnkd.in/ds_SzEUH

Shantanu Ladhwe

Experienced Machine Learning Engineering lead with a strong background in Machine Learning and MLOps across various domains including information retrieval (Search, Recommendations, RAG-systems & ChatBots), NLP, and Customer Analytics.

I specialize in guiding cross-functional teams of ML Engineers, Software Engineers, etc. to build scalable, event-driven ML solutions with a focus on robust MLOps practices using Python/PySpark and cloud platforms like AWS, GCP, etc. - (GCP Certified ML Engineer)

Currently, at HeyJobs, I have been fortunate to initiate and lead multiple complex ML projects that that have significantly outperformed previous benchmarks and generated substantial business value. Additionally, I also designed and lead the implementation of a new MLOps and monitoring framework that has been embraced across all Data Science teams within the company.

Additionally, I love learning the latest advancements in the ML space, always exploring new ideas to push boundaries and improve outcomes. I’m also dedicated to mentoring and sharing knowledge, focused on building systems that drive innovation and business outcome.

Head of AI/ML - AI Agents, RAG, NLP, Recommenders, Search & MLOps

Bharath Ganesh

70% of AI Engineers don’t know how to structure a repo

If your FastAPI + GenAI project is starting to look messy… this post is for you 👇

Here’s how we structure production-grade FastAPI based GenAI systems:
🔹 𝘀𝗿𝗰/ - 𝗖𝗼𝗿𝗲 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗟𝗼𝗴𝗶𝗰
We use a layered + domain-first architecture - the same structure that powers robust ML/AI systems in production.

Let’s break it down:
1️⃣ 𝗺𝗼𝗱𝗲𝗹𝘀/
→ SQLAlchemy ORM models
↳ Defines your database schema: tables, types, indexes
↳ E.g. academic paper metadata: title, authors, abstract

2️⃣ 𝘀𝗰𝗵𝗲𝗺𝗮𝘀/
→ Pydantic models for data validation
↳ Type-safe request/response structures
↳ Handles optional fields, default values, constraints

3️⃣ 𝗿𝗼𝘂𝘁𝗲𝗿𝘀/
→ FastAPI route definitions
↳ Logical grouping of endpoints (e.g., /search, /upload)
↳ Auto-generates clean Swagger/OpenAPI docs
✅ Exposes your system cleanly to devs, tools, and UIs.

4️⃣ 𝗿𝗲𝗽𝗼𝘀𝗶𝘁𝗼𝗿𝗶𝗲𝘀/
→ DB interaction layer
↳ Clean abstraction over SQL queries
↳ Makes business logic readable + testable
✅ No raw SQL scattered in your app. Easy to test and evolve.

5️⃣ 𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀/ - The brain of your application
→ Logic organized by domain, not just CRUD
e.g.:
↳ opensearch/ → Hybrid retrieval logic 
✅ Modular by design: swap, extend, or evolve features without chaos.

6️⃣ 𝗱𝗯/
→ Database engine config
↳ Connection pooling, retry logic, async sessions
✅ Rock-solid DB handling. No “random connection errors” in prod.

7️⃣ 𝗺𝗮𝗶𝗻.𝗽𝘆
→ FastAPI entry point
↳ Imports routes, applies middlewares, sets up CORS + logging
✅ Single source of truth for app startup.

8️⃣ 𝗺𝗶𝗱𝗱𝗹𝗲𝘄𝗮𝗿𝗲𝘀.𝗽𝘆 / 𝗲𝘅𝗰𝗲𝗽𝘁𝗶𝗼𝗻𝘀.𝗽𝘆
→ Reliability and observability layer
↳ Logs tracebacks
↳ Converts random errors into structured API messages
✅ Debugging and monitoring become predictable.

🔹 𝗕𝗲𝘆𝗼𝗻𝗱 𝘀𝗿𝗰/: 𝗜𝗻𝗳𝗿𝗮 + 𝗧𝗼𝗼𝗹𝗶𝗻𝗴
1️⃣ 𝗗𝗼𝗰𝗸𝗲𝗿𝗳𝗶𝗹𝗲 + 𝗰𝗼𝗺𝗽𝗼𝘀𝗲.𝘆𝗺𝗹
→ One-line infra bootstrapping:
↳ Starts: FastAPI, PostgreSQL, OpenSearch, Airflow, Ollama
✅ Works on every machine. No setup nightmares.

2️⃣ 𝗠𝗮𝗸𝗲𝗳𝗶𝗹𝗲
→ Simplifies commands like:
↳ make test, make lint, make up, etc.
✅ Standardized developer workflows.

3️⃣ 𝗽𝗿𝗲-𝗰𝗼𝗺𝗺𝗶𝘁-𝗰𝗼𝗻𝗳𝗶𝗴 . 𝘆𝗮𝗺𝗹
→ Linting + formatting (Ruff, Black, isort)
→ Prevents bad code from being committed
✅ Clean code. Enforced automatically.

4️⃣ 𝘁𝗲𝘀𝘁𝘀/
→ Pytest structure
↳ Unit tests with fixtures and mocks
✅ You can confidently refactor without breaking things.

5️⃣ 𝗻𝗼𝘁𝗲𝗯𝗼𝗼𝗸𝘀/
→ Analysis or test notebooks

PS: As you put the repo into production there would more components added, e.g. CI/CD, infrastructure, etc.


--
♻️ Repost to share with others :)
🔔 Follow me, Shantanu for production AI/ML/MLOps & careers
➕ Join 36.500+ serious AI/ML builders here: https://lnkd.in/ds_SzEUH

I am offering a 50% new year discount so you can begin your journey with Production AI Systems today.
↳ Code: PDL4GLSMEM
↳ Link: https://lnkd.in/dzdsTWz9

Listen, if you want to learn production Agentic RAG freely this is the FREE Repo you should check it out...

It has 23 tools freely available tools!
37,000+ are already building it.  

I am seeing so many new PAID projects coming up taking inspiration from our Repo - Code structure, RAG structure, Teaching structure, etc.
Shows this how valuable this Free Course is!

And we are not stopping here...
We are going to go deeper and cover every aspect of RAG!

As a hiring manager I would love to see one project with depth 
rather than 4 random surface level projects.

This project is designed to make 𝘆𝗼𝘂 learn and grow.  
(Not us - we’ve already built bigger systems in production.)  

Here's what 𝐚𝐜𝐭𝐮𝐚𝐥 building looks like👇

𝐖𝐞𝐞𝐤 𝟏: Docker orchestration of infra
(that doesn't crash when you need it)

𝐖𝐞𝐞𝐤 𝟐: Automated pipelines with Airflow
(run while you sleep, not when you babysit)  

𝐖𝐞𝐞𝐤 𝟑: OpenSearch Project BM25 search (know the basics first)
(beats your precious vectors 40% of the time)

𝐖𝐞𝐞𝐤 𝟒: Chunking strategies 
(spoiler: most tutorials get this wrong)

𝐖𝐞𝐞𝐤 𝟓: The complete RAG: Hybrid search & LLM
(high retrieval accuracy on real test data)

𝐖𝐞𝐞𝐤 𝟔: Redis caching + monitoring with Langfuse
(70% faster responses)

Week 7: Agentic RAG with LangGraph + Telegram integration
(this is how real RAG evolves)

𝐓𝐡𝐞 𝐫𝐞𝐬𝐮𝐥𝐭𝐬:
→ Zero API costs (runs locally, imagine that)
→ Processes arXiv papers daily (without breaking)
→ Catches edge cases automatically (because production is messy)
→ Real error handling (not print statements)

The entire codebase is open (with blog links): https://lnkd.in/dCYbsY3c

Here's my challenge:
𝟏. Fork it (if you can figure out Git)
𝟐. Run it locally (Docker skills required)
𝟑. Break it intentionally (find the weak spots)
𝟒. Fix what you broke (now you're learning)
𝟓. Add a feature (make it yours)

Already 2200 people are Starred it
Already 633 people forked it

This says it all!
FREE Tools
FREE Repo
FREE Production Learning

--
➕ Follow me, Shantanu for production AI/ML/MLOps & careers
➕ Join 37.500+ AI/ML builders here: https://lnkd.in/ds_SzEUH

taplio

Shantanu Ladhwe's Linkedin Analytics

Shantanu Ladhwe

Performance ScoreShantanu Ladhwe's LinkedIn Performance

What is Shantanu talking about?

Shantanu Ladhwe's Best Posts (last 30 days)

Ready to grow your LinkedIn presence?

taplio

.css-6ytdur{background-image:linear-gradient(284deg, rgba(39, 43, 72, 1) 0%, rgba(33, 47, 183, 1) 100%);color:transparent;-webkit-background-clip:text;background-clip:text;}Shantanu Ladhwe's Linkedin Analytics

Shantanu Ladhwe

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);}Shantanu Ladhwe's LinkedIn Performance

What is Shantanu talking about?

Shantanu Ladhwe's Best Posts .css-1emoo84{font-size:var(--chakra-fontSizes-md);font-weight:400;color:var(--chakra-colors-gray-600);}(last 30 days)

Ready to grow your LinkedIn presence?

Shantanu Ladhwe's Linkedin Analytics

Performance ScoreShantanu Ladhwe's LinkedIn Performance

Shantanu Ladhwe's Best Posts (last 30 days)