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.