About this role
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.