- Singapore Singapore
Lokasi Kerja
Penerangan Kerja
Tanggungjawab
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.
Peringatan Penting
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