About the Role Our partner is building an AI-first enablement team focused on evolving the enterprise architecture function through automation, lightweight applications, internal tooling, and generative AI. This is a hands-on engineering role for someone who can design, build, and ship practical software that saves time, improves governance workflows, and unlocks new capabilities. The ideal candidate is first and foremost a strong software engineer: someone with deep Python experience, solid development fundamentals, good architectural judgment, and a clear understanding of database design, system design, testing, maintainability, and operational quality.
What You’ll Do Partner directly with senior technologists and end users to understand workflows, pain points, and high-impact automation opportunities. Design and build internal applications, APIs, dashboards, automations, agents, and reusable AI-enabled workflows. Translate emerging GenAI usage, risks, and architectural patterns into practical governance tooling. Use AI coding assistants such as Cursor, Claude, Codex, Amp, GitHub Copilot, or similar tools to accelerate delivery while maintaining engineering rigor.
Review, debug, refactor, and improve AI-generated code for correctness, architecture, security, maintainability, performance, and testability. Design sound data models and data access patterns for internal applications and automation use cases. Prototype quickly, demo frequently, and iterate based on user feedback without compromising core software quality. Create reusable patterns, integrations, and guardrails that help others use AI-assisted development safely and productively.
Mentor and enable non-developers or technically adjacent stakeholders to build responsibly within appropriate guardrails. Required Qualifications Strong Development Foundations Non-Negotiable 7-12 years of professional python engineering experience, with strong hands-on development depth. Proven ability to design, build, debug, and operate production-quality software. Strong understanding of software architecture, design patterns, modularity, testing, maintainability, and operational excellence.
Experience building APIs, services, automations, internal tools, or web applications using modern frameworks such as FastAPI, Flask, Airflow, or similar. Solid understanding of database design, data modeling, SQL, data access patterns, and performance tradeoffs. Ability to make sound architectural decisions and explain tradeoffs clearly. Strong code review discipline, including the ability to identify brittle logic, poor abstractions, security concerns, scaling issues, and maintainability risks.
AI & Agent Technologies 1-3+ years of hands-on experience applying modern GenAI technologies to real use cases. Experience with LLM-based applications, AI agents, prompt engineering, RAG, workflow orchestration, or agentic systems. Familiarity with modern AI frameworks, protocols, and platforms such as LangGraph, MCP, Agent SDKs, vector databases, retrieval pipelines, or similar technologies.
Up-to-date knowledge of the rapidly evolving AI landscape, including emerging models, coding agents, orchestration patterns, evaluation approaches, and development tooling.