We’re looking for a strong AI engineer with roughly 4–5 years of experience to play a central role in our chat team. You’ll help shape the team’s technical direction, own the system design of day-to-day projects, maintain a high bar for engineering quality, and support junior developers through thoughtful design guidance and code reviews. This is a hands-on role for someone who combines strong programming fundamentals with practical experience building agentic systems. What you’ll do • Own the technical design and delivery of chat-team projects. • Shape engineering priorities, standards, and day-to-day technical decisions. • Turn product requirements into simple, maintainable system designs. • Write production Python and remain closely involved in implementation. • Design clear abstractions that reduce complexity without over-engineering. • Review pull requests for correctness, maintainability, test coverage, and overall design quality. • Help junior developers strengthen their programming and system-design judgment. • Diagnose production issues across application code, agent workflows, prompts, models, data, and infrastructure. • Improve the reliability, observability, latency, and cost of our chat systems. • Take initiative in making the team more AI-native by improving how we use coding agents throughout the development lifecycle. • Mentor other engineers in effective agentic coding workflows, including planning, implementation, testing, debugging, and code review. What we’re looking for • Approximately 4–5 years of professional software engineering experience. • Strong proficiency in Python, including writing idiomatic, typed, testable, and maintainable production code. • Strong programming fundamentals and consistently sound engineering judgment. • Experience designing, delivering, and operating production systems. • An ability to create useful abstractions while keeping systems simple and understandable. • Strong knowledge of API design, data modeling, concurrency, error handling, and observability. • Experience writing effective automated tests with tools such as pytest. • The ability to review code beyond surface-level concerns and explain the reasoning behind suggested changes. • Experience mentoring junior developers and improving the quality of their work. • Comfort taking ownership, identifying opportunities, and driving improvements across a team. • Clear written and verbal communication. Agentic systems experience Hands-on experience building LLM or agentic applications is required. Relevant experience may include: • Building tool-calling or multi-step workflows using LangGraph or a comparable agent framework. • Designing conversation state, context management, and execution flows. • Working with model APIs, structured outputs, retrieval, and grounding. • Supporting streaming responses and asynchronous execution. • Evaluating prompts, models, and end-to-end agent behavior. • Instrumenting and debugging LLM applications with Langfuse, LangSmith, or similar observability platforms. • Managing the reliability, latency, and cost of production LLM systems. We do not expect expertise in every named tool. We care about the underlying engineering judgment and the ability to learn or replace frameworks as the ecosystem changes. Agentic coding experience The candidate should be a power user of agentic coding workflows and understand how to use coding agents as engineering tools rather than simple code generators. They should be able to: • Use coding agents effectively across exploration, planning, implementation, testing, debugging, and review. • Provide agents with the right context, constraints, and verification steps. • Critically evaluate generated code for correctness, security, maintainability, and unnecessary complexity. • Design development workflows that keep engineers accountable for the resulting code. • Identify repeatable team workflows that can be improved through agents and automation. • Teach junior developers how to use coding agents effectively without weakening their engineering fundamentals. • Lead practical initiatives that make the team faster and more AI-native. What success looks like • The team makes clearer and more consistent technical decisions. • Projects have simple designs, well-defined boundaries, and useful tests. • Code reviews catch meaningful design and correctness issues early. • Junior developers receive actionable guidance and grow more independent. • The team develops effective and responsible agentic coding practices. • Production issues become easier to diagnose and resolve. • The codebase becomes easier to understand, change, and operate over time.