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Turing Intelligence builds and deploys production AI systems for large enterprises in banking, capital markets, asset management, and insurance. The Engagement Manager owns the end-to-end client outcome: from signed statement of work through production deployment, adoption, and renewal.
You will be the client's day-to-day executive counterpart and leader of AI Engagement Leads and Forward Deployed Engineers (FDEs). You own the engagement's scope, timeline, margin, and expansion opportunities. This is an account delivery leadership role—not project management, not engineering—where you keep senior stakeholders confident and hold the engagement to its commercial and delivery commitments.
Key Responsibilities:
Client Relationship: Serve as trusted counterpart to client sponsors from working teams through C-suite. Own steering committees, executive updates, and difficult conversations. Understand client business, organization, and politics deeply enough to anticipate needs and identify stakeholders who can accelerate or stall initiatives. Navigate regulated-enterprise complexity (model risk management, information security, data residency, procurement, legal, Responsible AI) as part of planning, not as a surprise. Bring bad news early with a plan.
Delivery (SOW to Production): Own engagements end-to-end. Structure SOWs with clear scope, milestones, dependencies, acceptance criteria, and value hypothesis before signature. Run engagement cadence, staffing, risk and dependency management, decision logs, and client communications. Make real-time scope and priority calls to protect the critical path. Define success as adoption and measured impact, not just on-time delivery. Set quality bar with engineering lead on production readiness, model evaluation, and acceptance testing.
P&L and Growth: Own engagement P&L against margin targets. Manage staffing mix, utilization, change orders, and scope creep. Forecast accurately and flag variance early. Identify and shape expansion: new use cases, adjacent business units, follow-on SOWs, renewals. Partner with BFSI General Manager to convert expansion opportunities. Report value delivered, risks, and key decisions on fixed cadence.
Team Leadership: Lead AI Engagement Leads, Forward Deployed Engineers, and delivery staff. Set direction, unblock obstacles, and hold bar on client-facing professionalism and delivery discipline. Partner with technical lead on architecture and staffing decisions; own outcome even where you don't own design. Codify reusable BFSI delivery playbooks, estimation models, and SOW patterns.
You will not write code but need sufficient technical fluency to scope AI work credibly, challenge engineers' plans, and explain trade-offs to client CTOs or model-risk teams without a translator.
Requirements:
Required:
- 8+ years in client-facing delivery or consulting
- At least 3 years leading engagements or accounts for financial services clients (banking, capital markets, asset management, insurance, or payments)
- Track record of owning engagement or account financials: margin, forecasting, staffing, change orders, renewals
- Led at least one AI, ML, or data-platform program into production at an enterprise; can speak to what the client changed as a result
- Executive presence and relationship depth: run steering committees, managed MD-level sponsors, retained accounts through difficult periods
- Technical fluency: can read architecture diagrams, understand how LLM-based systems are built and evaluated, ask questions that expose weak plans
- Based in New York metro area; able to be onsite with clients several days per week
- Collaborative leadership instincts; team player who keeps teams motivated under delivery pressure; role model in work ethic, accountability, and client commitment
Preferred:
- Engagement management at technology consultancy, systems integrator, or AI-native delivery firm; or delivery leadership role inside bank or insurer's technology or data organization
- Familiarity with BFSI regulatory context: model risk management, information security and third-party risk, data privacy and residency
- Experience in two-in-a-box model working alongside commercial account owner
