Senior GenAI Engineer

Requirements:

• 5-8 years of software engineering experience, including meaningful experience building and shipping AI-powered products in production.
• Strong CS fundamentals and engineering discipline; this is a builder role, not a research role.
• Can clearly explain difficult AI systems they've built, what failed, what they learned, and how they improved reliability over time.
• Deep understanding of LLM application architecture, including tool use, structured outputs, retrieval, orchestration, and production failure modes.
• Experience building agentic systems that perform multi-step reasoning and interact with tools or business systems.
• Prompt lifecycle management: treats prompts as versioned, testable, deployable artifacts, not strings scattered through the codebase.
• Strong RAG fundamentals: retrieval quality, chunking strategies, embeddings, evaluation, and knowledge system design.
• Experience designing evaluation frameworks and regression testing pipelines for AI systems.
• Strong understanding of observability for AI applications: tracing, monitoring, feedback loops, and production debugging.
• Experience balancing capability, latency, reliability, and cost when designing AI systems.
• Solid grasp of responsible AI, enterprise security, privacy, and governance considerations.
• Product-quality frontend skills and the ability to own AI experiences end-to-end.
• Strong backend engineering skills and the ability to build production-ready systems independently.

Good to have:

• Memory architectures: episodic memory, procedural memory, retrieval systems, and knowledge stores.
• Agent orchestration frameworks such as LangGraph, Pydantic AI, OpenAI Agents SDK, CrewAI, or custom runtimes.
• Experience building long-running autonomous workflows and event-driven agent systems.
• Voice, multimodal, and real-time AI systems.
• Fine-tuning experience and understanding of model internals beyond API consumption.
• Open-source model deployment and inference infrastructure.
• Experience building AI systems for enterprise workflows where correctness, auditability, and trust are critical.
• Experience with evaluation platforms, benchmark design, and AI quality measurement.
• Familiarity with financial systems, billing platforms, revenue operations, accounting, or fintech domains.
• Experience with MCP, tool ecosystems, and AI platform architectures.
• Startup experience and comfort operating in high-ownership environments.
• Not taking yourself too seriously.