AI Engineer

About the job

Overview

You will design and build agentic workflows — multi-step, multi-agent systems that reason, use tools, call external services, and recover from failure — and take them from prototype to reliable production. You turn business requirements into working agent systems: composing agents, designing the prompts and behaviors that drive them, wiring in the tools and data sources they need, choosing how the agents coordinate, and deciding where a human needs to stay in the loop. You build on top of our agent development tooling, so much of the surrounding machinery — identity, guardrails, logging, and monitoring — is handled for you; your focus is assembling those pieces into something that solves a real problem and behaves predictably even when the underlying models do not. The work is hands-on and product-facing: you translate requirements into designs, get them working against real data, harden them for production, and keep improving them once they are live.

Responsibilities

● Design and build agentic workflows — multi-step, multi-agent systems that reason, call tools, and recover from failure — owning them from design through production.

● Compose agents into reliable pipelines that integrate with external APIs, internal services, and data sources, with clear error handling and retry semantics.

● Design and iterate on the prompts and behaviors that drive each agent, treating them as versioned, tested artifacts rather than inline strings, and validating them against evaluation sets.

● Build the tools and integrations agents rely on — well-typed function interfaces or service endpoints — with attention to safety and correctness.

● Choose the right coordination approach for each task — a supervisor directing workers, a planner and executor, competing or critiquing agents, or a fixed sequence — and know when each fits.

● Decide where human review and approval belong, and build those checkpoints into longrunning workflows.

● Wire in state and memory appropriately — short-term working context and longer-term memory — so agents have what they need without leaking data across users or tenants.

● Build evaluation suites and regression checks that measure whether a workflow actually does its job — trajectory and outcome quality, not just pass/fail.

● Harden workflows for production — diagnosing timeouts, partial failures, unexpected model behavior, and state drift in long-running, non-deterministic systems.

● Instrument and debug agent behavior using tracing, logs, and cost and latency telemetry.

● Contribute to shared patterns, conventions, and reusable components that raise the quality bar for how agentic systems are built across the team.

● Partner with product and the broader engineering team to translate business requirements into agent designs that are practical to build, operate, and govern.

Qualifications

Required qualifications

● Strong software engineering fundamentals with production-grade Python — clean, tested code; comfortable with async patterns and long-running, stateful processes.

● Experience building or working with agentic or LLM-driven systems in a production or nearproduction setting — where a model drives control flow through tool use, multi-step reasoning, or agent-to-agent handoffs.

● Has composed multi-step or multi-agent workflows — orchestrating agents, managing shared state, chaining tool calls, and handling failures and retries.

● Practical prompt-engineering skill — can design prompts and system messages that make model behavior consistent, and has improved them against evaluations rather than by feel.

● Experience integrating with external APIs and services — REST, authentication, error handling, and designing for partial failure.

● Has built tools or integrations that a model or agent calls — function interfaces, schema design, or service endpoints.

● Disciplined about evaluation — can describe an eval suite they built for an agentic or LLM system, how they created the ground truth, and what it proved.

● Solid debugging instincts for distributed and non-deterministic systems — can reason through failures that span multiple agents or services.

● Writes meaningful tests for complex, stateful systems — unit, integration, and scenario-level coverage.

● Familiarity with agent or LLM observability — tracing, logging, or monitoring of model and tool calls.

● Background in backend engineering — APIs, queues, databases — that grounds you when workflows touch infrastructure.

● Exposure to multi-tenant systems and data-isolation concerns. Preferred qualifications

● Experience with durable or long-running workflow execution — systems where state must survive restarts, retries, or partial failures.

● Familiarity with agent memory — working memory, long-term memory, and managing context windows.

● Has worked with retrieval-augmented generation and reranking — wiring retrieval into an agent workflow and measuring its effect.

● Exposure to human-in-the-loop patterns — approval steps, escalation policies, and review workflows in production.

● Experience with agent orchestration frameworks or patterns (LangGraph or comparable).

● Familiarity with prompt-injection and adversarial risks in agent and retrieval systems, and how to mitigate them.

● Comfortable working against an SDK or framework abstraction — understands its constraints and when to escalate gaps.

● Open-source contributions to agent frameworks or related tooling.