About The Role

ABOUT THE ROLE

This is a mid-level AI Engineer position on the core product team at an early-stage AI startup, building agentic systems that automate complex, multi-step workflows across regulated and enterprise domains. You'll work across the full stack to ship production LLM-based services, with direct influence on product direction and user impact.

WHAT YOU'LL DO

Design, build, and maintain agentic systems that automate multi-step workflows across domains such as healthcare, legal, fintech, logistics, and compliance.
Own production retrieval-augmented generation (RAG) pipelines and retrieval infrastructure — including vector DBs, 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 closely with founders, product, and design to prioritize work, define success metrics, and iterate on user feedback and telemetry.

WHAT WE'RE LOOKING FOR

2–8 years of software engineering experience with a track record of shipping user-facing or backend products.
Hands-on 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 and how to apply them.
Experience building automated tests, evaluations, and monitoring for AI systems to ensure reliability beyond demo environments.
Experience designing API-driven, high-throughput systems and real-time product features.
Familiarity with agent or workflow frameworks (e.g., LangGraph, CrewAI) and orchestration tools (e.g., Temporal, Trigger) is a plus.
Background in multi-tenant or enterprise-ready systems, or experience in regulated industries (healthcare, fintech, legal), is a plus.
Familiarity with fine-tuning, parameter-efficient tuning, or multi-modal model integration is a plus.
Strong ownership mindset — comfortable driving features end-to-end from data model to deployment and monitoring.