Engagment Manager

Turing•New York, NY

3d•Hybrid

About The Position

Turing Intelligence builds and ships production AI systems for large enterprises. In banking, capital markets, asset management, and insurance our clients come to us with a hypothesis about where AI can move a P&L line, and we turn it into working software that runs in their core workflows, in a regulated environment, with a measured result. The Engagement Manager owns that outcome: from signed SOW through production deployment, adoption, and renewal. You are the client's day-to-day executive counterpart, the leader of AI Engagement Leads, Forward Deployed Engineers (FDEs), and the owner of the engagement's scope, timeline, margin, & expansion. This is not a project-management seat and not an engineering seat. It is an account delivery leader who runs a book of business inside a client, keeps senior stakeholders confident, and holds the engagement to its commercial and delivery commitments. You will not write code, but you need enough technical fluency to scope AI work credibly, challenge your engineers' plans, and explain trade-offs to a client CTO or model-risk team without a translator.

Requirements

Collaborative Engagement Manager with sound leadership instincts, a team player who keeps teams motivated under delivery pressure and leads by example, serving as a role model in work ethic, accountability, and client commitment.
8+ years in client-facing delivery or consulting, with 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.
You have led at least one AI, ML, or data-platform program into production at an enterprise and can speak to what the client changed as a result.
Executive presence and relationship depth: you have run steering committees, managed MD-level sponsors, and retained accounts through difficult periods.
Technical fluency: you can read an architecture diagram, understand how an LLM-based system is built and evaluated, and ask the questions that expose weak plans. You do not need to code.
Based in the New York metro area and able to be onsite with clients several days per week.

Nice To Haves

Engagement management at a technology consultancy, systems integrator, or AI-native delivery firm; or a delivery leadership role inside a 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 a two-in-a-box model working alongside a commercial account owner.

Responsibilities

Be the trusted counterpart to client sponsors from working teams to managing directors and C-suite. Own the steering committee, the executive updates, and the hard conversations.
Understand the client's business, org, and politics well enough to anticipate what they need before they ask, and to know which stakeholders can stall or accelerate an initiative.
Navigate regulated-enterprise complexity as part of the plan, not a surprise at go-live: model risk management, information security review, data residency, procurement, legal, and Responsible AI requirements.
Protect the relationship through delivery problems. Bring bad news early, with a plan.
Own engagements end to end. Structure SOWs with clear scope, milestones, dependencies, acceptance criteria, and a value hypothesis before signature.
Run the engagement: cadence, staffing, risk and dependency management, decision logs, client communications. Make real-time calls on scope and priority to protect the critical path.
Hold the standard that success is adoption and measured impact, not "delivered on time." Define the baseline and KPIs up front and report against them.
Set the quality bar with your engineering lead: production readiness, evaluation of model-backed components, and acceptance testing are part of "done."
Own the engagement P&L against Turing Intelligence's margin targets. Manage staffing mix, utilization, change orders, and scope creep to protect gross margin. Forecast accurately and flag variance early.
Identify and shape expansion: new use cases, adjacent business units, follow-on SOWs, renewals. Partner with the BFSI GM, who owns the account relationship and revenue number, to convert them.
Report value delivered, risks, and key decisions to client sponsors and Turing Intelligence leadership on a fixed cadence.
Lead AI Engagement Leads, Forward Deployed Engineers, and delivery staff. Set direction, unblock, and hold the bar on client-facing professionalism and delivery discipline.
Partner with your technical lead on architecture and staffing decisions; own the outcome even where you don't own the design.
Codify what works into reusable BFSI delivery playbooks, estimation models, and SOW patterns.