Job description

Job Summary

WHAT YOU WILL OWN

Features across the agent runtime, RAG, memory, evaluation and the Studio, working inside a senior engineer's track. You will build the reference agents, the knowledge-base pipeline, the eval datasets and much of the pilot feedback work — the parts of the product where AI quality is decided.

MUST HAVE

Strong Python with modern tooling (typing, Pydantic, async, pytest); can work in TypeScript / React when needed.

Hands-on with LLM APIs: tool / function calling, structured outputs, prompt iteration, token and cost awareness; has shipped at least one LLM-powered feature to real users.

Built a RAG pipeline or search feature: chunking, embeddings, a vector database, retrieval evaluation.

Comfortable with PostgreSQL and Redis; understands REST API design and event-driven basics.

Works with Docker, Git and CI daily; writes tests without being asked.

Curious and rigorous: reads model behaviour from traces, forms a hypothesis, tests it.

GOOD TO HAVE

LangGraph or another agent framework. Temporal. Kubernetes. Experience building evaluation datasets or with LLM-as-judge. Prior work on enterprise integrations.

Key Responsibilities

RESPONSIBILITIES

Implement features end to end — API, data model, tests, telemetry — from a design agreed with your track lead.

Build and tune agents: prompts, tool schemas, structured outputs, routing rules; measure them with the eval harness rather than by feel.

Own the knowledge-base pipeline (ingestion, chunking, embedding, retrieval quality) and the memory features.

Turn pilot transcripts and traces into eval cases and fixes.

Contribute to the Studio (React / TypeScript) when your feature needs a UI.

Skill Requirements

MUST HAVE

Strong Python with modern tooling (typing, Pydantic, async, pytest); can work in TypeScript / React when needed.

Hands-on with LLM APIs: tool / function calling, structured outputs, prompt iteration, token and cost awareness; has shipped at least one LLM-powered feature to real users.

Built a RAG pipeline or search feature: chunking, embeddings, a vector database, retrieval evaluation.

Comfortable with PostgreSQL and Redis; understands REST API design and event-driven basics.

Works with Docker, Git and CI daily; writes tests without being asked.

Curious and rigorous: reads model behaviour from traces, forms a hypothesis, tests it.

Other Requirements

Someone shared one job with you. ASAI keeps finding the rest.

Scored, not searched. Every role ranked against your actual profile.

Alerts as often as hourly. Reach new roles while the pile is still small.

Skill gaps, spelled out. See exactly which requirements you don't meet yet.

Verified jobs, only. Say no to ghost jobs. Your time deserves respect.

Someone shared one job with you. ASAI keeps finding the rest.

Scored, not searched. Every role ranked against your actual profile.

Alerts as often as hourly. Reach new roles while the pile is still small.

Skill gaps, spelled out. See exactly which requirements you don't meet yet.

Verified jobs, only. Say no to ghost jobs. Your time deserves respect.

Job description

Job Summary

WHAT YOU WILL OWN

MUST HAVE

Strong Python with modern tooling (typing, Pydantic, async, pytest); can work in TypeScript / React when needed.

Hands-on with LLM APIs: tool / function calling, structured outputs, prompt iteration, token and cost awareness; has shipped at least one LLM-powered feature to real users.

Built a RAG pipeline or search feature: chunking, embeddings, a vector database, retrieval evaluation.

Comfortable with PostgreSQL and Redis; understands REST API design and event-driven basics.

Works with Docker, Git and CI daily; writes tests without being asked.

Curious and rigorous: reads model behaviour from traces, forms a hypothesis, tests it.

GOOD TO HAVE

LangGraph or another agent framework. Temporal. Kubernetes. Experience building evaluation datasets or with LLM-as-judge. Prior work on enterprise integrations.

Key Responsibilities

RESPONSIBILITIES

Implement features end to end — API, data model, tests, telemetry — from a design agreed with your track lead.

Build and tune agents: prompts, tool schemas, structured outputs, routing rules; measure them with the eval harness rather than by feel.

Own the knowledge-base pipeline (ingestion, chunking, embedding, retrieval quality) and the memory features.

Turn pilot transcripts and traces into eval cases and fixes.

Contribute to the Studio (React / TypeScript) when your feature needs a UI.

Skill Requirements

MUST HAVE

Strong Python with modern tooling (typing, Pydantic, async, pytest); can work in TypeScript / React when needed.

Hands-on with LLM APIs: tool / function calling, structured outputs, prompt iteration, token and cost awareness; has shipped at least one LLM-powered feature to real users.

Built a RAG pipeline or search feature: chunking, embeddings, a vector database, retrieval evaluation.

Comfortable with PostgreSQL and Redis; understands REST API design and event-driven basics.

Works with Docker, Git and CI daily; writes tests without being asked.

Curious and rigorous: reads model behaviour from traces, forms a hypothesis, tests it.