OpenAI

Technical Deployment Lead, Forward Deployed Engineering - London

OpenAI2 weeks ago
Location

London, UK

Type

Full Time

Salary

GBP 180,000 – 280,000

Level

Senior

Role

Lead

Posted

Jul 10, 2026

Full TimeSenior

The role

Summary

OpenAI's Forward Deployed Engineering team seeks a Technical Deployment Lead to define how complex AI systems are delivered to enterprise customers. You'll own end-to-end delivery of 0-1 prototypes through scale, translating business outcomes into technical execution plans while embedding with customers to ensure adoption. This high-trust role requires 7+ years of customer-facing technical leadership, deep expertise in shipping AI/LLM systems, and the ability to operate across product, research, and customer teams to drive measurable business impact.

What you'll do

Own technical delivery planning and execution: Define and manage technical delivery plans across multiple interdependent workstreams. Translate business objectives into detailed roadmaps with clear milestones, dependencies, and acceptance criteria. Track progress, manage critical path sequencing, and make real-time trade-offs between scope and priority to ensure successful project outcomes.
Drive day-to-day engineering execution: Run daily execution across OpenAI Forward Deployed Engineers, Researchers, and Customer Engineering teams. Keep delivery unblocked through proactive dependency management, real-time status tracking, and swift decision-making on technical priorities to maintain momentum and meet agreed timelines.
Embed with customer teams and lead adoption: Embed directly with customer teams to understand workflows, map success criteria, and translate business requirements into technical delivery plans. Lead comprehensive onboarding, drive adoption initiatives, manage organizational change, and ensure customer teams can sustain and scale solutions post-deployment.
Partner with Product and Research leadership: Coordinate with OpenAI's Product and Research teams to ensure platform components and research workstreams align with deployment goals and timelines. Share field signals, customer insights, and emerging patterns to guide product roadmap prioritization and architectural decisions.
Codify patterns and build reusable solutions: Extract solution patterns from individual deployments and package field signals and learnings. Document best practices, evaluation frameworks, and architectural patterns to improve product capabilities, accelerate future deployments, and establish operating leverage across the customer base.
Measure and demonstrate business impact: Set impact hypotheses, establish baseline metrics and KPIs aligned with customer business outcomes. Own pre-deployment and post-deployment measurement, ROI analysis, and executive reporting to demonstrate value realization and inform future engagement strategies.

What we look for

Technical

AI/LLM systems architecture and deploymentProven expertise in shipping production AI and LLM systems end-to-end. Deep understanding of solution patterns, integration approaches, common production pitfalls, and how to operationalize AI capabilities within enterprise environments at scale.
System design and technical fluencyStrong ability to move fluidly between high-level system architecture and execution-level details. Comfortable diving into customer workflows and data constraints, sketching technical architectures, pressure-testing designs, and making sound technical trade-offs under ambiguity.
Complex project orchestrationDemonstrated capability managing large, complex, high-stakes projects with multiple interdependent technical workstreams, external dependencies, and cross-functional teams. Ability to simplify complexity, sequence work logically, and maintain clear visibility into progress and blockers.
Change management and adoption strategyExperience designing and executing organizational change management, adoption, and readiness programs to ensure customers successfully integrate and scale new solutions. Understanding of how to translate technical capabilities into business benefits and drive user adoption.

Education

Technical background or degreeBachelor's degree in Computer Science, Engineering, Mathematics, Physics, or equivalent technical field. Alternatively, equivalent practical experience demonstrating deep technical fluency and systems thinking.

Experience

Customer-facing technical leadership7+ years of customer-facing technical delivery leadership, ideally in roles such as Customer Solutions Engineer, Technical Program Manager, Customer Engineering Lead, or Solutions Architect. Track record of leading large, complex customer engagements where outcomes depended on tight coordination and fast decision-making.
High-stakes customer deliveryDemonstrated success leading high-stakes customer projects involving multiple teams, significant technical complexity, and high business impact. Experience working across sales, product, and engineering to deliver solutions that customers depend on for critical workflows.
Domain expertise in major sectorsDeep expertise and credibility in at least one major industry vertical such as healthcare, energy, financial services, semiconductors, IT, or manufacturing. Domain knowledge helps you understand customer workflows, regulatory context, and positioning of solutions within specific markets.
Ambiguity navigation and strategic thinkingTrack record of thriving in high-ambiguity environments with incomplete information. Ability to pattern-match across situations, recognize broader trends, and connect specific customer needs to scalable, reusable solutions rather than one-off customizations.

Skills

Required skills

AI/LLM system implementationHands-on experience deploying Large Language Models and AI systems in production environments, including RAG architectures, fine-tuning, prompt engineering, and integration patterns.
Technical project managementAdvanced project management capabilities including roadmap planning, dependency tracking, risk management, stakeholder coordination, and delivery of complex multi-team initiatives.
Customer requirement translationAbility to elicit, understand, and translate ambiguous customer requirements into clear technical specifications, acceptance criteria, and delivery plans that align with business outcomes.
Cross-functional collaborationStrong ability to work effectively across engineering, product, research, and customer teams. Can influence without authority, facilitate alignment, and resolve conflicts between competing technical and business priorities.
Executive communicationAbility to translate complex technical trade-offs and architectural decisions into clear, compelling narratives for executive stakeholders and business leaders. Strong presentation and written communication skills.
Systems thinking and architectureCapability to reason about complex distributed systems, understand architectural trade-offs, design scalable solutions, and evaluate technical approaches against business constraints.

Nice to have

Enterprise SaaS deployment experienceExperience deploying complex SaaS solutions or platforms within large enterprises, navigating procurement, security, compliance, and change management challenges.
Solutions architecture backgroundExperience in solutions architecture or pre-sales engineering roles where you designed custom solutions for specific customer needs and worked closely with sales and implementation teams.
Startup and scale-up environmentsBackground working in early-stage, high-growth technical environments where you wore multiple hats, navigated rapid change, and built processes and systems from scratch.
Research collaborationExperience working alongside researchers, translating research innovations into production systems, and understanding the transition from research to deployed applications.
API-first platform developmentFamiliarity with modern API-first platforms, microservices architectures, and building applications on top of third-party AI/ML platforms and APIs.

Compensation & benefits

Salary

GBP 180,000 – 280,000 (annual)

Stock options

Available

Benefits

Equity and stock options

Competitive stock option grants providing ownership stake in OpenAI's future. Equity compensation aligns your interests with company success and provides long-term wealth building opportunity.

Comprehensive health coverage

Medical, dental, and vision insurance plans covering you and your family. OpenAI covers substantial portions of premiums with options for HSA accounts and other wellness benefits.

Flexible work arrangement

Hybrid work model with 3 days per week in office in London. Flexibility supports work-life balance while maintaining in-person collaboration and culture building with team members.

Relocation assistance

Comprehensive relocation support and assistance if you're relocating to London for this role, including logistical support and financial assistance for moving expenses.

Professional development and learning

Investment in your growth through conference attendance, training programs, and learning budgets. OpenAI values continuous learning and staying current with AI research and industry developments.

Paid time off

Generous paid vacation and personal time policies. Mental health days and wellness time are encouraged, supporting overall well-being and preventing burnout.

Retirement planning

Competitive retirement and pension benefits, including contributions that help you build long-term financial security.

Mission-driven impact

Opportunity to work on cutting-edge AI technology with direct impact on how organizations harness AI to solve important problems. Being part of OpenAI's mission to ensure AI benefits humanity.

Interview process

  1. 1
    Initial screening and background review — Recruiter screens your background to verify customer-facing technical delivery experience, AI/LLM system shipping track record, and alignment with Forward Deployed Engineering mission. Expect questions about your most complex customer project and outcomes achieved.
  2. 2
    Technical depth conversation — Technical discussion with a Forward Deployed Engineer or Engineering Lead to assess your AI/LLM system architecture knowledge, technical sequencing instincts, and ability to reason through complex deployment scenarios. Be prepared to discuss specific systems you've architected and trade-offs you've made.
  3. 3
    Customer delivery case study — In-depth conversation about a high-stakes customer engagement you led. Focus on how you navigated ambiguity, coordinated across teams, managed dependencies, drove adoption, and measured impact. You'll need to articulate the business context, technical challenges, your leadership approach, and measurable outcomes.
  4. 4
    Systems thinking and pattern recognition — Discussion evaluating your ability to step back from execution details, recognize patterns across deployments, and think strategically about scalable solutions. May include scenario-based questions about how you'd approach a novel customer deployment or emerging technical pattern.
  5. 5
    Executive presence and communication — Assessment of your ability to communicate complex technical concepts to non-technical executives, make trade-offs legible to business leaders, and translate strategy into day-to-day technical execution. May include a presentation or storytelling component.
  6. 6
    Domain expertise validation — Discussion of your domain expertise in your sector (healthcare, energy, financial services, semiconductors, IT, etc.). Interviewer assesses depth of your understanding and how it helps you elevate solution framing and customer credibility.
  7. 7
    Final round with leadership — Conversation with a senior leader or department head focused on alignment on career aspirations, how you operate in high-autonomy environments, your approach to extreme ownership, and cultural fit with OpenAI's mission-driven organization.
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OpenAI

OpenAI

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OpenAI is an American artificial intelligence research organization developing advanced AI models like GPT. Focused on ensuring AI benefits humanity, it creates tools for natural language processing and generative AI applications.

San Francisco, California, United StatesFounded 2015openai.com

Tech Stack

Languages
PythonJavaScript/TypeScript
Frameworks
LangChainVector Database IntegrationsFastAPI or Flask
Databases
PostgreSQLVector Databases (Pinecone, Weaviate, Milvus)
Tools
OpenAI APIGit and version controlDocker and containerizationProject management platforms
Other
Cloud platforms (AWS, GCP, Azure)Monitoring and observabilityRetrieval-Augmented Generation (RAG) patterns

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