About the Role

We are seeking a Technical Deployment Lead to turn ambitious cybersecurity deployments into shipped, adopted systems. In this role, you will lead day-to-day technical delivery for complex engagements: translating business outcomes into technical plans, coordinating Forward Deployed Engineers (FDEs), researchers, customer engineers, Product, and customer teams, and carrying solutions from prototype through production adoption. This role will report to DeployCo delivery leadership and partner closely with Forward Deployed Engineering, Product, Research, Customer Success, Partnerships, and customer stakeholders.

You will partner with enterprise security teams to deploy AI across threat investigation, incident response, and vulnerability management.

Your day-to-day will be mapping customer workflows and success criteria, building delivery plans, tracking dependencies, sequencing workstreams, unblocking technical teams, making scope tradeoffs, communicating risks, and driving readiness and change management so deployed systems land in real workflows. You will also codify reusable patterns, surface field insights, and help turn complex deployments into repeatable operating muscle.

Success in this role will look like deployments that deliver measurable customer value, reach production reliably, drive adoption, and produce reusable patterns for future engagements. You will be measured by delivery reliability, quality of technical execution, customer trust, operating leverage, judgment under pressure, and the strength of field signal shared back to Product and Research.

In this role, you will:

• Own technical delivery plans for customer workstreams, translating business objectives into milestones, dependencies, acceptance criteria, and operating rhythms.
• Run day-to-day execution across DeployCo, OpenAI, and customer teams, keeping work sequenced, risks visible, blockers resolved, and critical paths protected.
• Embed with customer security teams and relevant business and technology functions to map workflows, shape requirements, and ensure solutions are adopted in production.
• Partner with FDEs, researchers, product, customer engineers, and customer technical teams to move prototypes through MVP, production readiness, launch, and scale.
• Make pragmatic tradeoffs on scope, sequencing, resourcing, and quality while maintaining trust with technical and executive stakeholders.
• Own value cases and impact measurement, including hypotheses, baselines, KPIs, adoption tracking, and readouts to sponsors.
• Codify reusable delivery patterns, implementation playbooks, evals, and field insights that improve future deployments and inform product roadmap priorities.

You may thrive in this role if you:

• Have 5+ years of customer-facing technical delivery, technical program management, deployment, consulting, solutions engineering, or implementation leadership experience.
• Can credibly discuss both established and emerging threats, including AI-enabled attack and defense, translating them into practical detection and response workflows for both practitioners and customer leadership
• Have led complex, ambiguous, cross-functional cybersecurity deployments in Fortune 500 or similarly complex enterprises, where outcomes depended on tight coordination and fast decision making.
• Are technically fluent enough to pressure-test architectures, integration plans, data and workflow constraints, and production readiness without needing to be the deepest engineer in every domain.
• Move fluidly between system-level strategy and execution-level detail, from executive objectives to ticket-level dependencies.
• Communicate clearly with business leaders, technical teams, and customer stakeholders, especially when tradeoffs involve risk, scope, speed, or adoption.
• Bring strong customer empathy and are willing to spend time onsite with customers when it accelerates delivery and trust.
• Have practical interest or experience in AI/LLM systems and are eager to learn solution patterns, evaluation practices, and production pitfalls.
• Operate well in high-ambiguity environments and bring enough structure to make complex work simple, visible, and shippable.