Tech Lead - Data Science

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 Range= 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.