Context Plane Python Engineer

Overview

In this role you will lead the development of a greenfield data platform that connects the firm’s data mesh and knowledge sources to AI agents. You will own end-to-end components, from ingestion to governed retrieval, shaping the architecture and engineering practices across the team. This hands-on position emphasizes collaboration with AI, product, and data science colleagues to deliver measurable capabilities. You’ll mentor teammates, drive secure, scalable solutions, and advance enterprise AI-assisted development.

Responsibilities

• Design, build, and maintain Python backend services and data pipelines to load knowledge into a knowledge graph and vector store
• Develop the serving layer for graph and vector retrieval, GraphRAG, response assembly, and a Model Context Protocol endpoint for downstream agents
• Promote reusable components by extracting them into a shared core library to reduce duplication
• Integrate with data sources and services across the firm, including enterprise AI and LLM gateways
• Ensure quality through automated tests, code reviews, observability, and secure, resilient service design
• Collaborate with AI, product, and data science colleagues to translate use cases into capabilities
• Contribute to design discussions and agile ceremonies; mentor teammates to raise engineering standards
• Drive adoption of enterprise AI-assisted engineering practices to improve quality, speed, and outcomes; promote reusable patterns
• Apply SDLC tools and AI-assisted development/automation capabilities to maximize value and automation

Key requirements

• Formal training or certification in software engineering with advanced applied experience
• Proven track record building production-grade Python backend services and data pipelines
• Strong API design skills (e.g., FastAPI), automated testing, CI/CD, and source control
• Experience designing data ingestion/integration pipelines at scale with focus on data quality and resilience
• Proficiency with AWS and containerized services (Docker/ECS)
• Ability to own components end-to-end from design to deployment and observability
• Strong cross-functional collaboration across engineering, product, and data science
• Hands-on experience using enterprise AI-assisted development tools; ability to assess AI outputs for correctness, performance, and security
• Understanding of responsible AI in engineering, including data sensitivity, secure handling, resiliency, and security expectations

• collaboration
• mentorship
• communication
• Python backend development
• FastAPI API design
• CI/CD and automated testing

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