Senior AI Engineer
Job Description
- 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.