Forward Deployed Engineer - Managed AI
TITAN · USA
About this role
## Forward Deployed Engineer — Managed AI ### About Titan Titan is building the AI workforce for IT. We deploy AI agents across our portfolio of Managed Service Providers and enterprise customers—agents that monitor, triage, and resolve work across the service desk, infrastructure, security, identity, endpoint, and application operations. The result: faster resolutions, better uptime, and more proactive, personalized support delivered 24/7. ### About the Team (Managed AI) Managed AI is Titan’s program for companies making AI part of how they run. We manage new and existing AI platforms, advise leadership on AI strategy and governance, and build custom agents, software, and integrations. ### About the Role Forward Deployed Engineers (Managed AI) take a client workflow from shortlisted idea to an agent running in the client’s tenant—typically within a quarter. You own scoping, build, deployment, and handover, working directly with client teams and their IT. You’ll: - Write connectors into **Microsoft 365** and line-of-business systems - Build agents on **Claude, ChatGPT, or Copilot** - Ship agents under the client’s own controls - Measure success by agents live in production, user adoption, and delivery within the fixed fee - Partner closely with **vCAIOs**, other Forward Deployed Engineers, Product, and GTM ### What You’ll Do (Key Responsibilities) - Join **vCAIO-led opportunity assessments** and identify workflows where data is spread across systems - **Scope each build**: systems it reads, actions it takes, user group, success metric + baseline, and call out bottlenecks outside the client’s control - Build agents, connectors (e.g., **Microsoft Graph, SharePoint, Teams, Outlook, client APIs, MCP servers**) and skills needed for each engagement - Deploy into client tenants with **least-privilege access**, an entry in the **governance register**, and documentation suitable for audits/DDQs - Measure agents against baseline, train users, and hand off to ongoing maintenance - Turn what works into reusable connectors, skills, evals, and templates ### Typical Engagement Timeline - **Month 1:** Platform connected; shadow AI + spend baseline (review tenant inventory) - **Month 2:** vCAIO-led agent opportunity assessment (interviews, workflow scoring, baseline, scope) - **Month 3:** First agent build (build, deploy to a named user group, measure, hand over) - **Month 4+:** Further agents scoped per project (maintain live agent; scope/build next) ### Example Projects - **Medical device manufacturer:** Customer site activation tracker (Teams/SharePoint/Outlook + tracker) to flag stalled sites and missing owners - **Hedge fund:** Investor DDQ responder that drafts answers with a source for every line - **Industrial manufacturer:** Supplier quality monitor that flags expiring certifications/repeat defects and drafts corrective action requests ### You Might Thrive If You Have - **4+ years** of engineering experience, including client-facing work - Experience building/shipping **LLM-powered agents** and understanding how model behavior impacts UX - Ability to write production-grade code in **TypeScript, Python, or similar** - Strong knowledge of **Microsoft 365** (Graph API, SharePoint, Teams, Outlook) and app permissions/consent - Ability to run scoping sessions with business leaders and write scopes a CFO will sign - Bias toward shipping working agents quickly (e.g., to 10 users in ~3 weeks) - Extreme ownership and a security-first mindset fo
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