Tech Lead - Data Science

Mumbai-Maharashtra-Indialead

Data ScienceData & AI

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Quick Summary

Key Responsibilities

document ingestion, chunking strategies, embeddings, vector search (Azure AI Search / FAISS / pgvector), re-ranking, and grounding for agent knowledge.

Requirements Summary

LangChain/LangGraph, AutoGen, CrewAI, Semantic Kernel, or equivalent (at least one in production). Strong understanding of LLM application patterns: prompt engineering, function/tool calling, struc

Technical Tools

Data ScienceData & AI

About the Role

~1 min read

We are hiring a Tech Lead - Data Scientist to play a key role in building our Agentic AI Platform — a system of autonomous, tool-using AI agents that plan, reason, and execute complex business workflows end-to-end. The ideal candidate combines strong ML fundamentals with hands-on experience in LLM-based application development, agent orchestration frameworks, and Microsoft Azure cloud services. You will architect and ship production-grade agentic solutions, mentor junior team members, and set technical direction for GenAI initiatives across the organization.

Experience -8 to 12 yrs

Responsibilities

~2 min read

→Design, build, and productionize multi-agent systems — including planning, tool calling / function calling, memory, and orchestration — using frameworks such as LangGraph, AutoGen, CrewAI, or Semantic Kernel.

→Develop RAG pipelines end-to-end: document ingestion, chunking strategies, embeddings, vector search (Azure AI Search / FAISS / pgvector), re-ranking, and grounding for agent knowledge.

→Integrate agents with enterprise systems and APIs via tool/function calling and Model Context Protocol (MCP) or similar connector patterns.

→Build and maintain LLM evaluation frameworks for agentic workflows — task-completion metrics, hallucination detection, trajectory analysis, LLM-as-judge pipelines, and A/B testing.

→Implement guardrails, safety, and governance for agents: prompt-injection defense, content filtering, role-based tool permissions, human-in-the-loop checkpoints, and audit logging.

→Fine-tune and optimize LLMs where needed (LoRA/PEFT, prompt optimization, model routing, latency/cost trade-offs) on Azure OpenAI / Azure AI Foundry.

→Design, build, and evaluate classical ML models (classification, regression, forecasting, NLP) where they complement agentic workflows.

→Own LLMOps/MLOps for the platform: experiment tracking, prompt versioning, CI/CD, observability and tracing (LangSmith, Azure Monitor, OpenTelemetry), drift monitoring, and retraining strategies.

→Collaborate with data engineers on data quality, availability, and governance across Azure Data Lake, Databricks, and Synapse Analytics.

→Translate ambiguous business problems into agentic AI solutions; present architecture decisions and results to senior stakeholders.

→Mentor junior data scientists, lead code/design reviews, and champion engineering best practices.

Requirements

~1 min read

Bachelor's or Master's degree in Computer Science, Data Science, Engineering, Mathematics, or a related field.

8-10 years of professional experience in data science / ML, with at least 3 years building LLM or GenAI applications in production.

Hands-on experience with agentic frameworks: LangChain/LangGraph, AutoGen, CrewAI, Semantic Kernel, or equivalent (at least one in production).

Strong understanding of LLM application patterns: prompt engineering, function/tool calling, structured outputs, RAG, agent memory, and multi-agent orchestration.

Expert-level Python (pandas, NumPy, scikit-learn, async programming, API development with FastAPI).

Hands-on experience with Azure services: Azure OpenAI / AI Foundry, Azure ML, Azure AI Search, Azure Databricks, Azure Data Factory, or Synapse.

Experience with vector databases and embeddings (Azure AI Search, Pinecone, Weaviate, Qdrant, FAISS, or pgvector).

Solid grounding in classical ML: supervised/unsupervised learning, model evaluation, and hyperparameter tuning; experience with TensorFlow or PyTorch.