• Agentic AI Development:

Design, build, and deploy agentic AI systems using frameworks such as LangChain, LangGraph, and related libraries.

Develop and deploy multi-agent systems capable of autonomous decision-making, reasoning, planning, and collaboration.

• RAG Pipelines:

Implement and optimize retrieval-augmented generation (RAG) systems, ensuring agents can access and incorporate external knowledge sources for grounded, accurate responses.

• LLM Engineering:

Fine-tune and prompt-engineer LLMs for task-specific reasoning, planning, and dynamic adaptation.

Work with LLM/SLM APIs, embeddings, and advanced generative AI techniques.

• Enterprise AI Platform:

Lead the development of enterprise-grade AI platforms integrating LLMs, RAG, embeddings, and agentic AI protocols.

Implement and standardize Model Context Protocol (MCP) for consistent context management across models and agents.

• MLOps & Observability:

Establish and enforce best practices for MLOps, monitoring, and observability, ensuring scalable and maintainable AI solutions.

• Applied AI Prototyping:

Rapidly prototype, experiment, and iterate to improve AI agent capabilities.

• Collaboration & Research:

Participate in the full research cycle: literature review, data exploration, experimentation, and presentation of findings.

Collaborate effectively with other engineers, researchers, and data scientists.

Contribute to the documentation and standardization of technical code and practices.