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.

Many wouldn't like this:
If I’m hiring for an AI Engineer…
I’m not going to ask 5 RAG flows or 10 agent flows.

Here’s what I really want to know 👇

1. Can you design a system end-to-end? from ingestion to serving? what are the bottlenecks?
2. How would you estimate costs? how to reduce it?
3. How would you reduce latency? what is a good tradeoff of latency vs quality? 
4. Do you really need self-hosted LLMs? When is it needed? 
5. How would you fine-tune language models on user behavior? which framework? what about model serving?
6. How would you construct the dataset, what about loss function? what about MLOps? 
7. Which database would you use and why? Vector DB? SQL? NoSQL?
8. What metrics would you track? How? 
9. What about system monitoring? How would you debug failure cases?
10. What about feedback loop? how would we track and evaluate?
11. How would you make the system more deterministic?
12. How would you replace embedding models and backfill embeddings without any downtime?
13. What are the fallback mechanisms?

My favourite:
1️⃣ How would you solve this problem without LLMs or vector DBs?
2️⃣ Can you solve the problem with classical IR, rules, heuristics?
3️⃣ How would you make the system more deterministic?
4️⃣ Can you explain tokenization and embeddings from scratch?
5️⃣ What happens during fine-tuning? optimizer, learning rate, layer freezing? - not just “let's use LoRA”

These are just a few top-of-mind.

The reason:
I’ve seen engineers spend months building something flashy and complex that never made it to production.
I want engineers who understand the fundamentals, not just the hype.

I don’t want a flashy agent.
I want a resilient system.

Do you agree?

PS: This can vary by role, but I’d be very cautious hiring someone with shallow foundations.

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Shantanu Ladhwe

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

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!

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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.

Bharath Ganesh

If you are into AI/ML Engineering

This one video covers the production gap that kills 90% of ML projects.

"System Design Course" by freeCodeCamp (975K+ views)
Free. 1 hour. Changes everything.

The problem with ML education: 
They teach you to build models. 
They don't teach you to serve 1000 users.

As someone building RAG & AI Agents: 
Model/LLM matters 10%. 
Infrastructure matters 90%.

🔷 What this covers:
→ Caching & CDNs: Cut RAG costs 70% 
→ Load Balancers: Handle 10x traffic without crashing
→ Database Sharding: Scale vector storage properly
→ Computer Architecture: 10x faster training and inference
→ CI/CD Pipelines: Deploy confidently

📌 Video link - https://lnkd.in/ddSYvGpc

Must watch!
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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

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