Applied AI Engineer

Los Angeles, CA • Full-time • Entry level

About the role

Azimuth already runs on AI systems the firm owns and directs: research agents, revision loops, sourcing workflows, and an internal CRM that must stay a truthful mirror of relationships rather than a busy-looking database. We need a builder who will turn that into a durable internal platform: the custom AI stack, the CRM and its integrations, and the proprietary agents the team actually uses every day. This role will encompass product engineering for a small, high-standard firm: reliable agents, clean data contracts, human-in-the-loop review where judgment belongs to people. You will work next to the operators who live in these tools, so the bar is production usefulness.

Responsibilities

• Own Azimuth's internal AI platform end to end: agent runtimes, tool wiring, evaluation, logging, and the workflows that support research, harvest/revision loops, sourcing, and delivery.
• Build and evolve the firm's CRM and related systems so they stay truthful and safe for handling of confidential client context.
• Design and maintain proprietary agents the team runs in production (research, drafting under house style, ops helpers), with clear contracts, tests, and failure modes operators can trust.
• Integrate the stack with the tools the firm already uses (Google Workspace, Drive comment loops, email/calendar where authorized, Git-backed skills and docs, ATS where relevant).
• Enforce the firm's standard in software: refusal paths, source requirements, human gates on anything that could invent people or touch external systems unsafely.
• Improve reliability and operability: evals, traces, dashboards that matter, docs another engineer or power user can run, and cost/latency discipline.
• Partner with the delivery team on what to automate, what must stay human, and how new capabilities show up in real workflows rather than as unused prototypes.

Requirements

• 3 or more years building production software, with clear ownership of systems other people depended on.
• Hands-on experience with LLM-backed applications in production: agents or tool-using workflows, retrieval, evaluation, and the unglamorous work of making them reliable.
• Strong generalist engineering: Python is the default here; comfort with APIs, data models, auth, and shipping small internal products quickly.
• Product judgment. You can talk to operators, cut scope, and choose the boring correct design over the impressive fragile one.
• Clear written communication. You document systems so the firm is not dependent on your memory.

Nice to have

• Self-hosted or tightly controlled model serving experience (vLLM, Ollama, or equivalent), or strong opinions about when local vs API is right.
• Prior work on CRM, ATS, workflow engines, or other systems of record where bad writes are expensive.
• Familiarity with consulting, recruiting, professional services, or other high-trust operational domains.
• Experience with human-in-the-loop review UIs, background job systems, or multi-agent orchestration that had to survive real failure.