Key Responsibilities

- Design, develop, and deploy multi-agent AI systems using open-source or enterprise-level frameworks.
- Translate complex business requirements into scalable agent-based AI architectures.
- Implement Generative AI and RAG pipelines, leveraging enterprise-grade tools such as LangChain, LlamaIndex, or similar.
- Architect and build modular and extensible agentic AI systems, using frameworks like LangGraph, CrewAI, AutoGen, etc.
- Collaborate with cross-functional teams (Product, Data, Engineering) to ship AI-powered features into production.
- Build APIs, microservices, or full-stack solutions (as needed) to support AI capabilities.
- Stay current on the latest advancements in LLMs, multi-agent coordination, and open-source AI tools.
- Ensure system reliability, scalability, and performance in a production-grade enterprise environment.

Required Skills Qualifications

- Proficient in Python with strong software engineering fundamentals (OOP, design patterns, unit testing, CI/CD).




- Demonstrated experience building production-ready Generative AI applications (LLMs, RAG, chat-based tools).
- Hands-on knowledge of LangChain, LlamaIndex, or similar enterprise-grade GenAI libraries.
- Deep understanding of agent-based AI architectures and frameworks like LangGraph, CrewAI, AutoGen, etc.
- Experience with multi-agent orchestration, task decomposition, memory management, and reasoning loops.
- Familiarity with vector databases, embedding models, and prompt engineering best practices.
- Ability to take business requirements and build intelligent agentic systems with measurable results.
- Robust understanding of REST APIs, cloud platforms (AWS/GCP/Azure), and optionally DevOps practices.

Nice to Have

- Experience with graph algorithms and graph-based reasoning systems.
- Familiarity with cognitive architectures or planning algorithms.
- Contributions to open-source AI projects or research publications.

📌 Senior AI Engineer (Bengaluru)
🏢 ThoughtFocus
📍 Bengaluru