Forward Deployed Engineer - Managed AI

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 is faster resolutions, better uptime, and more proactive, personalized support, delivered 24/7.

In under a year we've raised over $100M from General Catalyst, 8VC, and CalPERS, acquired market-leading MSPs like RFA, and put agents into production in large enterprises. We've automated 100K+ tickets across 400+ customers. Our tech team has talent from Scale AI, CoreWeave, Vanta, Dropbox, Meta, FiveRings, TwoSigma, Amazon, Harvard, Stanford, and MIT.

Joining Titan means helping redefine how work gets done in IT services, and building a $100B company.

Managed AI is Titan's program for companies making AI part of how they run. We manage their new and existing AI platforms, advise their leadership on AI strategy and governance, and build custom agents, software and integrations. Our clients range from hedge funds to medical device makers, and each one brings new systems, workflows and problems to solve. Forward Deployed Engineers (Managed AI) are the builders behind it.

Forward Deployed Engineers (Managed AI) take a client workflow from shortlisted idea to an agent running in the client's tenant, usually within a quarter. You own scoping, the build, the deployment and the 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, and ship them under the client's own controls.

You own and measure success by agents live in production, adoption by the user group each was built for, and delivery inside the fixed fee we quoted. You work closely with vCAIOs, other Forward Deployed Engineers, Product and GTM.

This role is remote (US).

• Join vCAIO-led opportunity assessments and find the workflows where data is spread across systems nobody reads in the same week

• Scope each build: the systems it reads, the actions it takes, its user group, its success metric and baseline, and say so when the bottleneck sits outside the client's control

• Build the agents, connectors (Microsoft Graph, SharePoint, Teams, Outlook, client APIs, MCP servers) and skills each engagement needs

• Deploy into client tenants with least-privilege access, an entry in the governance register, and documentation that holds up to an auditor or a DDQ

• Measure each agent against its baseline, train its users, and hand it into ongoing maintenance under the platform

• Turn what works in one engagement into connectors, skills, evals and templates the next one can reuse

A first agent goes live in month three of a client's program, at a fixed fee, measured against a baseline taken in month two.

• Month 1, platform connected, shadow AI and spend baseline: review the tenant inventory; note the systems and data you will need

• Month 2, vCAIO-led agent opportunity assessment: join interviews, score workflows on value, risk and effort, capture the baseline, write the scope

• Month 3, first agent build: build, deploy to a named user group, measure, hand over

• Month 4+, further agents scoped per project: maintain the live agent; scope and build the next one

These are the kinds of agents you would build in your first months.

• Medical device manufacturer, customer site activation tracker: reads the commercial team's Teams channels, account files in SharePoint, Outlook threads and the current tracker; follows each new customer site through contracting, training and first use; flags sites that have stalled or lack a named owner. The commercial team sees every stalled site in one place, every week.

• Hedge fund, investor DDQ responder: reads past DDQs, policies and the compliance library; drafts answers to a new questionnaire with a source for every line. First draft in hours instead of two weeks of IR and compliance time.

• Industrial manufacturer, supplier quality monitor: reads supplier certificates, incoming inspection results from the ERP and nonconformance reports; flags expiring certifications and repeat defects; drafts the supplier corrective action request. Quality catches supplier issues before they reach the line, and every request leaves an audit trail.

• Bring 4+ years of engineering experience, including client-facing work

• Have built or shipped LLM-powered agents and know how model behavior shapes the product experience

• Write and review production-grade code in TypeScript, Python, or a comparable stack

• Know Microsoft 365 as a data source: Graph API, SharePoint, Teams, Outlook, and how app permissions and consent work in a tenant

• Can run a scoping session with a business leader, say no to a weak use case, and write a scope a CFO will sign

• Would rather put a working agent in front of 10 users in three weeks than a perfect one in front of none in three months

• Take extreme ownership of problems and delivery, and handle client data with a security mindset

• Forward deployed, solutions or sales engineering experience at a SaaS, AI or consulting company

• Built on MCP: servers, connectors, or skills for Claude, ChatGPT or Copilot

• Location: Remote (US)

• Travel: Up to 5%, for on-site discovery and launch

• Compensation: $150,000–$200,000 base

• Interview process: recruiter screen; technical call; take-home or live build of a small agent against a mock Microsoft 365 tenant; client scoping role-play with a vCAIO; founder conversation