AI Engineer

What You'll Do

- Design and implement stateful multi-agent networks and/or workflows using LangGraph and/or LangChain.

- Build and optimise end-to-end RAG pipelines, focusing on high-precision retrieval, semantic search, and the integration of diverse data sources (vector DB, graph DB, RDBMS, etc).

- Architect and implement multi-layered guardrails to ensure agent actions remain within business scope, enterprise safety and policy boundaries.

- Build and maintain high-performance AI microservices, ensuring they are optimised for OCI-compliant environments.

- Use of LLM evaluation frameworks to quantitatively measure agent performance.

- Partner with software teams to define data contracts and integrate information flow from AI layer to software backend and frontend.

Technical Requirements

- Expert-level proficiency in Python, specifically for asynchronous AI applications.

- Mastery of LangGraph and LangChain for building production-grade, stateful systems with human-in-the-loop verification patterns.

- Proven success in developing RAG capabilities, including experience with chunking strategies, advanced techniques like query expansion, re-ranking, hybrid search, etc.

- Deep hands-on experience with vector DB, graph DB, NoSQL and RDBMS.

- Experience building guardrails to mitigate risks such as prompt injection and data leakage.

- Experience integrating AI evaluation into CI/CD pipelines (eg, Jenkins, GitLab CI).

- Experience serving AI agents via FastAPI, packaging and deploying in OCI-compliant environments and OCP.

- Proficiency in AI tracing, observability and logging tools to identify and fix bottlenecks in complex reasoning paths.

- Experience implementing semantic caching and designing for parallel LLM invocation.

- Knowledge of prompt engineering and context engineering techniques.