Tech Lead

• Lead a team of AI engineers and data scientists to architect, develop, and deploy intelligent AI systems.
• Drive end-to-end design of LLM-based, multi-agent, and autonomous AI frameworks, ensuring scalability, governance, and security.
• Mentor junior engineers, conduct code reviews, and enforce engineering best practices.
• Partner with product, data, and cloud teams to align technical delivery with business outcomes.

• Define architectural blueprints for RAG pipelines, MCP-based agentic systems, and AI integration layers.
• Lead implementation of Databricks Lakehouse-based MLOps pipelines using MLflow, Delta Lake, and Feature Store.
• Architect high-availability AI workloads across AWS / Azure / GCP environments.

• Build and orchestrate AI agents using LangChain, CrewAI, AutoGen, LangGraph, or Semantic Kernel.
• Establish frameworks for tool invocation, agent collaboration, and Model Context Protocol (MCP) communication.
• Drive experimentation with new AI techniques multi-modal models, autonomous planning, or symbolic reasoning.

• Implement best practices for Responsible AI, observability, and model explainability.
• Define AI evaluation frameworks, safety protocols, and quality assurance mechanisms for LLMs and agents.
• Ensure compliance with organizational and cloud governance policies.

Ideal Candidate Profile

AI / ML Engineering

• Deep expertise in Python (PyTorch, TensorFlow, scikit-learn, pandas, NumPy).
• Strong grounding in model design, feature engineering, and fine-tuning.
• Experience with vector databases (FAISS, Pinecone, ChromaDB) and semantic search.

Generative AI & NLP

• Proven experience in LLM fine-tuning, RAG systems, prompt optimization, and conversational AI.
• Implementation of advanced text intelligence systems (summarization, Q&A, sentiment, classification).

Agentic AI & Frameworks

• Deep hands-on experience with LangChain, CrewAI, AutoGen, LangGraph, or Semantic Kernel.
• Expertise in multi-agent orchestration, reasoning engines, and context-aware decision-making.
• Implementation of Model Context Protocol (MCP) for agent-to-system interoperability.

Databricks & MLOps

• Strong experience with Databricks (Spark, Delta Lake, MLflow).
• Design and automation of CI/CD pipelines for ML models.
• Experience with model registry, drift detection, rollback, and versioning.

Cloud & Infrastructure

• Advanced knowledge of AWS, Azure, or GCP AI services (SageMaker, Azure ML, Vertex AI).
• Experience with container orchestration (Docker, Kubernetes).
• Familiarity with Infrastructure as Code (Terraform / CloudFormation).

Soft Skills & Leadership Attributes

• Ability to lead and inspire a high-performing AI engineering team.
• Strong communication skills for technical and executive audiences.
• Analytical thinker who can translate business problems into AI-driven solutions.
• Passion for mentoring, innovation, and staying ahead in the rapidly evolving AI landscape.

Preferred / Nice to have

• Exposure to reinforcement learning, planning-based or multi-modal agents.
• Experience integrating AI with enterprise SaaS platforms or APIs.
• Contributions to open-source AI frameworks or research.
• Familiarity with knowledge graphs and cognitive architecture. c