Engagement Manager
- Compensation
- Not disclosed
- Location
- New York, New York, United States
- Work setup
- Not specified
- Seniority
- Lead
- Employment
- Not specified
- Experience
- 3+ years
- Category
- Not specified
- Posted
Skills
Must have
- 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, accountab
- 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 paymen
- 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 have
- 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.
Full description
About Turing
Turing’s mission is to accelerate superintelligence to drive real economic progress. Headquartered in San Francisco, Turing works with frontier AI labs to generate high-quality datasets, reinforcement learning environments, and frontier research benchmarks that improve model capabilities in software engineering, enterprise knowledge work, and advanced STEM reasoning. In software engineering, Turing is the largest and longest-running data provider in the category. Turing also works with Fortune 500 enterprises across financial services, life sciences, healthcare, retail, automotive, and CPG to build and deploy end-to-end agentic AI systems inside mission-critical workflows. By operating on both sides, Turing closes the loop between frontier research and enterprise deployment, turning real-world deployment signals into better data, evaluations, and more capable models. Learn more at .
About the role
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 a pod of Forward Deployed Engineers (FDEs), and the owner of the engagement's scope, timeline, margin, and 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.
What you'll ownClient relationship
- 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.
Delivery, SOW to production
- Own engagements end to end. Structure SOWs with clear scope, milestones, dependencies, acceptance criteria, and a value hypothesis before signature.