AI Engineer (Mid-Level)

About the Role

Join a fast-moving, pre-seed-backed AI startup building the next generation of agentic systems that automate complex, multi-step workflows across regulated and enterprise domains — including healthcare, legal, fintech, logistics, and compliance. As a mid-level AI Engineer on the core product team, you'll own production LLM-based services end-to-end, collaborate closely with founders and product, and ship features that deliver measurable impact for real users.

What You'll Do

• Design, build, and maintain agentic systems that automate complex, multi-step workflows across regulated industries.
• Own production retrieval-augmented generation (RAG) pipelines and retrieval infrastructure, including vector databases, embeddings, and indexing for domain-specific search at scale.
• Implement multi-agent orchestration, tool-calling, memory, and reasoning components to deliver robust AI-driven user experiences.
• Develop evaluation and safety infrastructure to measure model performance, surface regressions, and enforce enterprise-level trust and reliability.
• Ship full-stack AI products from MVP to enterprise-grade — designing APIs and data models, writing frontend and backend code, and operating production systems with CI/CD, monitoring, and testing.
• Collaborate with founders, product, and design to prioritize work, define success metrics, and iterate based on user feedback and telemetry.

What We're Looking For

• Must-haves: 2–8 years of software engineering experience with demonstrated delivery of shipped user-facing or backend products.
• Practical experience deploying LLMs or LLM-based services in production, including prompt design, orchestration, and tool integration.
• Proficiency across the stack: Python plus TypeScript/React (or equivalent), cloud platforms (AWS or GCP), and relational or NoSQL databases.
• Working knowledge of RAG patterns, vector databases, embeddings, and retrieval pipelines, with sound judgment on when to apply each approach.
• Experience building automated tests, evaluations, and monitoring for AI systems to ensure reliability beyond demos.
• Experience designing API-driven, high-throughput systems and real-time product features.
• Nice-to-haves: Experience with agent or workflow frameworks (e.g., LangGraph, CrewAI) and orchestration tools (e.g., Temporal, Trigger).
• Familiarity with fine-tuning, parameter-efficient tuning, or multi-modal model integration.
• Background building multi-tenant or enterprise-ready systems, or prior experience in regulated industries such as healthcare, fintech, or legal.