Applied AI Engineer

newline; Los Angeles (CA); On-site; USD 120,000 - 160,000; Full time

Job summary

Azimuth Partners in Los Angeles, CA is seeking an Applied AI Engineer to own and evolve our internal AI platform and CRM systems. You will build durable tools, integrate with existing workflows, and design production-grade agents with strong safety and testing practices.

You will collaborate with operators to ensure production usefulness, focusing on reliable runtimes, data contracts, and human-in-the-loop governance within a high-trust environment.

Qualifications

• 3+ years building production software with ownership.
• Hands-on experience with LLM-backed applications in production: agents or tool-using workflows.
• Strong generalist engineering: Python focused, APIs, data models, auth, and shipping small internal products quickly.
• Product judgment: ability to talk to operators, cut scope, choose robust design over fragile
• Clear written communication and system documentation.

Responsibilities

• Own Azimuth's internal AI platform end to end: agent runtimes, tool wiring, evaluation, logging, and workflows for research and sourcing.
• Build and evolve the firm's CRM and related systems for confidential client context handling.
• Design and maintain proprietary agents with contracts, tests, and safety/failure modes.
• Integrate the stack with Google Workspace, Drive loops, email/calendar where allowed, Git-backed docs.
• Enforce firm software standards: refusal paths, source requirements, and human gates for sensitive actions.
• Improve reliability: evals, traces, dashboards, documentation for operators or power users.
• Partner with delivery to automate where appropriate and align capabilities with real workflows.

Skills

LLM-backed apps

Python

APIs

Data models

Authentication

Documentation

Communication

Tools

CRM

ATS

Workflow engines

Job description

Applied AI Engineer

Azimuth Partners Los Angeles, CA

5 days ago Over 200 applicants

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.

What you will do

• 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.

What you bring

• 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.
• Seniority level Entry level
• Employment type Full-time
• Job function Engineering and Information Technology
• Industries Technology, Information and Internet

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