Python Software Engineering Manager

We are looking for a Python Software Engineering Manager to build an AI-powered financial assistant platform. You will partner with a small senior team and technical stakeholders to ship chat, reporting, and bespoke interfaces into investment workflows. Responsibilities Collaborate with a small, highly capable team of hands-on engineers in a startup-style environment with minimal formality Partner directly with sophisticated technical stakeholders without needing to interface with non-technical clients or users Elicit low-level requirements from high-level task definitions and account for edge cases before implementation Design systems and deliver production-grade implementations with strong autonomy and self-governance Influence product direction and contribute to architecture decisions Build and maintain systems using cutting-edge technology without legacy constraints Adopt new languages and technologies as the team evolves its stack Optimize AI applications across quality, latency, reliability, and cost Review, test, and validate AI-generated code produced through coding agents Monitor AI behavior in production and perform failure analysis and regression testing Maintain a high bar for quality, reliability, security, and maintainability across deliverables Requirements Proven track record in software development with 8+ years of hands-on experience in Python, React (TS), or other programming languages Strong willingness and readiness to learn Python quickly if you are not yet proficient Hands-on experience with FastAPI or other Python frameworks, plus AWS, Kubernetes, and Terraform High autonomy and self-governance to take loosely defined tasks from requirements gathering through design and delivery Comfort working in a startup environment with continuous learning of new languages and technologies High standards for quality and reliability in software delivery Solid experience with FE work, including TS/JS and React/NextJS/Node Practical proficiency in AI-assisted programming methods and tools, including coding agents for code generation Background in AI evaluation and quality engineering, including automated evaluations, test datasets, regression testing, and failure analysis Understanding of AI security and production controls, including data privacy, access control, prompt-injection risks, guardrails, and auditability Ability to optimize AI applications across quality, latency, reliability, and cost Capability to learn and adopt new technologies rapidly as the AI ecosystem evolves Advanced proficiency in English (C1+)