Related skills
node.js python rest apis rag llm📋 Description
- Design and implement LLM-powered features via model APIs with reliability.
- Architect and maintain RAG pipelines linking models to knowledge bases and live data.
- Manage context window strategy: what information enters the model, format, and compression.
- Design and implement agentic workflows for multi-step autonomous tasks.
- Build guardrails and output validation to constrain model behavior and ensure compliance.
- Develop reusable agent primitives, prompts, and workflow components for reuse.
🎯 Requirements
- 5+ years software engineering; 1-2 years with LLMs in production.
- Strong Python or Node experience; building API-backed backend services.
- Hands-on with an orchestration or execution framework.
- RAG architecture, vector databases (Pinecone, pgVector, AWS OpenSearch), semantic search.
- Context management: summarisation, chunking, session splitting, memory strategies.
- Experience with REST APIs and integrating with third-party services.
🎁 Benefits
- Experience with MCP or similar tool-integration standards.
- LLMOps: tracing/observability (LangSmith, Datadog) and model versioning.
- Exposure to multi-agent architectures and orchestration patterns.
- AI output validation, context safety, governance in financial services.
- Familiarity with AWS or cloud infra and containerised deployments (Docker, Kubernetes).
- Ability to communicate technical concepts clearly to technical and non-technical partners.
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