Tech Lead - Data Science

Johnson Controls; Mumbai; Hybrid; INR 4,000,000 - 7,000,000; Full time

Benefits offered by this job

Competitive salary

Job summary

Johnson Controls in Mumbai seeks a Tech Lead - Data Scientist to architect and ship production-grade agentic AI solutions. You will mentor juniors, set technical direction for GenAI initiatives, and drive end-to-end LLM-enabled workflows across Azure cloud services.

The role requires 8–12 years of data science/ML experience, with hands-on production work on LLM/GenAI projects and strong Python skills. Collaborative leadership and a focus on governance are essential.

Qualifications

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

Responsibilities

• Design, build, and productionize multi-agent systems — planning, tool calling, memory, and orchestration.
• Develop RAG pipelines end-to-end: ingestion, chunking, embeddings, vector search, re-ranking, grounding.
• Integrate agents with enterprise systems via tool calling and Model Context Protocol or similar.
• Build and maintain LLM evaluation frameworks for agentic workflows and A/B testing.
• Implement guardrails, safety, governance for agents: prompt-injection defense, content filtering, audit logging.
• Fine-tune and optimize LLMs (LoRA/PEFT) on Azure OpenAI / Azure AI Foundry.
• Design, build, and evaluate classical ML models to complement agentic workflows.
• Own LLMOps/MLOps: experiment tracking, versioning, CI/CD, observability, drift monitoring.
• Collaborate with data engineers on data quality across Azure Data Lake, Databricks, Synapse.
• Translate business problems into agentic AI solutions; present architecture to stakeholders.
• Mentor junior data scientists and lead code/design reviews.

Skills

Data Science

LLM/GenAI

Python

MLOps

Azure Cloud

RAG pipelines

Multi-agent systems

SQL

GitHub Actions

DevOps

Education

Master's degree in CS/DS

Bachelor's degree

Tools

LangGraph

AutoGen

CrewAI

Semantic Kernel

Azure OpenAI

FAISS

pgvector

LangSmith

Azure Monitor

OpenTelemetry

Databricks

Synapse

Job description

About the Role

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

Key Responsibilities

• 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.

Required Skills & Qualifications

• 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.

• Strong SQL skills and experience working with large‑scale data.

• Proven MLOps/LLMOps experience: MLflow, prompt/model versioning, CI/CD (Azure DevOps or GitHub Actions), and production monitoring.

• Ability to evaluate and mitigate LLM‑specific risks: hallucination, prompt injection, data leakage, and cost/latency constraints.

Good to Have

• Experience with Model Context Protocol (MCP), OpenAI Assistants/Agents SDK, or Anthropic tool‑use APIs.

• Microsoft certifications: AI-102 (Azure AI Engineer), DP-100 (Azure Data Scientist Associate).

• Experience fine‑tuning open‑source LLMs (Llama, Mistral, Phi) using LoRA/QLoRA and serving via vLLM or Azure ML endpoints.

• Familiarity with observability/tracing for agents : LangSmith, Langfuse, Arize Phoenix, or OpenTelemetry.

• Knowledge of containerization and deployment: Docker, Kubernetes (AKS), Azure Container Apps.

• Big data experience with Apache Spark (PySpark) via Azure Databricks.

• Experience with knowledge graphs, graph RAG, or semantic layers for agent grounding.

• Contributions to open‑source GenAI/agentic projects or published technical content.

Technical Stack

Category

Tools & Technologies

Languages

Python, SQL

Agentic & GenAI

LangGraph, LangChain, AutoGen, CrewAI, Semantic Kernel, MCP, Azure OpenAI (GPT-4o), Anthropic Claude

RAG & Vector Search

Azure AI Search, FAISS, Qdrant, pgvector, Hugging Face embeddings

ML/AI Frameworks

scikit-learn, XGBoost, PyTorch, TensorFlow, Hugging Face Transformers

Cloud Platform

Microsoft Azure (AI Foundry, Azure ML, Databricks, Data Factory, Synapse)

LLMOps / MLOps

MLflow, LangSmith/Langfuse, Azure DevOps, GitHub Actions, Docker, AKS

APIs & Serving

FastAPI, Azure Functions, Azure Container Apps

Data & BI Tools

Power BI, Pandas, PySpark, Jupyter

Storage & DB

Azure Blob Storage, Azure Data Lake, SQL Server, Cosmos DB

What We Offer

• Competitive salary and performance‑based incentives.

• Opportunity to architect a greenfield agentic AI platform from the ground up.

• Azure and AI certification sponsorship plus a continuous learning budget.

• Technical leadership pathway and mentorship opportunities.

• Access to cutting‑edge GenAI tooling, compute, and cross‑domain AI projects.

• Flexible hybrid working model and collaborative culture.