AI Solutions Engineer, Talent Acquisition

Job description

Job responsibilities

• Build AI agents and automations that solve real recruiting problems — sourcing, screening, outreach, scheduling, candidate research, pipeline operations, reporting — with measurable ROI tied to recruiter time saved,…
• Own TA's AI and automation roadmap end-to-end — prioritization, sequencing, communication, and progress read-outs to TA leadership and stakeholders.
• Partner closely with Anduril's Corporate Technology team to understand the company's AI tooling, model access, infrastructure, security posture, and roadmap — and design solutions that ride those rails rather than…
• Form and defend a clear point of view on where AI and automation will and won't drive value across the recruiting function, and proactively surface opportunities the team hasn't asked for yet.
• Lead rigorous build-vs.-buy assessments — evaluating vendors, internal platforms, and custom builds against scalability, total cost, safety, compliance, and long-term maintainability.
• Ship pragmatically — strategically prioritized initiatives most of the time, but also fast, home-grown solutions for ad hoc team requests when that's the rightcall.
• Partner with the TA Enablement team to drive baseline AI literacy across the recruiting org — internal training, documentation, prompt patterns, guardrails, and best practices that recruiters and sourcers can actually…
• Own the safety, compliance, and governance posture of every AI and automation system you ship — PII handling, role-based access, audit trails, model risk, data residency, and ITAR/CUI considerations where applicable.

Job requirements

• 4+ years of hands-on technical experience building production software, with at least 1–2 years of that focused on applied AI / LLM-based systems (agents, tool use, RAG, evaluations, automations).
• Genuinely curious about the recruiting / TA domain (prior experience strongly preferred) — willing to sit with recruiters and sourcers, learn their workflows, and build for their problems rather than the most…
• Strong software engineering fundamentals — APIs and integrations, version control, testing, CI/CD, basic infrastructure, and deployment.
• Production experience building AI agents or LLM-driven automations for use across a growing team (250+ people) — including prompt design, tool use, retrieval, evaluation, and the failure modes that come with…
• Experience integrating with SaaS APIs and stitching workflows across multiple tools — comfort reading API docs, handling auth, rate limits, pagination, and webhooks.
• Demonstrated ability to identify high-leverage opportunities in an operational function, ship working systems against them, and quantify the impact.
• Strong build-vs.-buy judgment — can rigorously evaluate vendors and internal platforms against custom builds on scalability, cost, safety, compliance, and maintainability.
• Comfortable owning a roadmap end-to-end — prioritization, communication to non-technical leadership, and clear written read-outs.