Job description

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. COMPENSATION & BENEFITS - Salary: $180,000 – $400,000 USD annually (reflecting a wide band across base and equity depending on experience). - Early-stage equity in a venture-backed startup.

Requirements

Must-haves

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.
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.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aillmci/cdpythontypescriptreactawsgcpapi

Platform: ashby
Effort: quick