OpenAI

Technical Deployment Lead, Forward Deployed Engineering - Stockholm

OpenAI2 weeks ago
Location

Stockholm, Sweden

Type

Full Time

Salary

USD 145,000 – 220,000

Level

Senior

Role

Technical Deployment Lead

Posted

Jul 10, 2026

Full TimeSenior

The role

Summary

As a Technical Deployment Lead at OpenAI's Forward Deployed Engineering team in Stockholm, you will own end-to-end technical delivery of complex AI/LLM systems to enterprise customers, translating business outcomes into executable technical plans while managing cross-functional teams of FDEs, researchers, and customer engineers. This high-autonomy role requires deep technical project management expertise, AI systems knowledge, and exceptional ability to operate in ambiguity, embedding with customers to ensure deployments deliver measurable value and drive adoption. You will directly influence OpenAI's product roadmap through field signals while maintaining delivery excellence across multiple interdependent workstreams.

What you'll do

Own Technical Delivery Planning: Define and execute comprehensive technical roadmaps for multiple interdependent workstreams. Translate business objectives into detailed milestones, manage dependencies, establish acceptance criteria, and sequence delivery across cross-functional teams to protect critical paths and ensure on-time deployment of AI/LLM systems.
Drive Day-to-Day Engineering Execution: Lead daily standup coordination, track progress across OpenAI FDE and customer engineering teams, unblock delivery bottlenecks, and make real-time trade-offs on scope and priority. Maintain momentum across technical workstreams while ensuring dependencies are resolved and sequencing decisions are sound.
Embed with Customer Teams for Production Deployment: Spend significant time onsite with customers (25-50% or higher) to map their workflows, understand success criteria, design integration strategies, and shape tools that align with their operational needs. Lead adoption, change management, and ensure production systems integrate seamlessly into customer environments.
Partner with Product and Research Teams: Collaborate with OpenAI's Product and Research organizations to ensure platform components and research workstreams are delivered on schedule to support deployment timelines. Extract field signals and translate customer needs into product roadmap priorities and architectural improvements.
Codify Reusable Solution Patterns and Evaluation Frameworks: Document and package repeatable AI/LLM solution patterns and evaluation methodologies from deployments. Create frameworks that accelerate future customer engagements, improve product capabilities, and establish best practices for production AI systems.
Own Value Cases and ROI Measurement: Define impact hypotheses, establish baseline metrics, and set KPIs for each customer deployment. Execute pre- and post-deployment measurement, quantify business value delivered, and report outcomes to executive sponsors to demonstrate delivery impact and justify continued investment.

What we look for

Technical

AI/LLM Systems Architecture KnowledgeDeep understanding of large language model systems, solution patterns for AI integration, production deployment considerations, and common pitfalls in enterprise AI deployments. Ability to architect and pressure-test complex AI system designs.
Technical Project Management ExpertiseProficiency in managing large-scale, technically complex projects with multiple dependencies, tight coordination requirements, and high stakes outcomes. Strong sequencing instincts, dependency management, and trade-off decision-making capability.
Systems-Level Technical UnderstandingAbility to move fluidly between architecture-level decisions and execution-level details. Comfortable diving into customer data, workflows, and constraints; capable of sketching technical solutions and translating ambiguous problems into shipped systems.
Integration and Implementation FundamentalsPractical knowledge of integrating enterprise systems, API design, data pipeline architecture, and production deployment patterns. Understanding of how to evaluate and implement solution trade-offs in real-world customer environments.

Education

Bachelor's Degree in Computer Science or Related FieldFoundational technical education in computer science, software engineering, or related discipline. Strong technical foundation required to engage credibly with platform architects and research teams on system design decisions.
Advanced Technical Knowledge in AI/MLAdvanced coursework, certifications, or equivalent professional experience demonstrating deep understanding of AI/ML systems, large language models, and enterprise AI deployment best practices.

Experience

Customer-Facing Technical LeadershipMinimum 7+ years of experience leading large, complex, high-stakes customer engagements where outcomes depended on tight coordination and fast decision-making. Track record of successfully navigating customer relationships, managing expectations, and delivering measurable business impact.
AI/LLM Systems Deployment ExperienceDemonstrated experience shipping and deploying AI/LLM systems in production environments. Understanding of solution patterns, integration basics, production pitfalls, and lessons learned from real-world AI implementations.
Enterprise Sector ExpertiseDeep expertise in at least one major enterprise sector such as healthcare, energy, financial services, semiconductors, or enterprise IT. Domain knowledge that enables credible solution framing and ability to elevate customer conversations at executive levels.
High-Ambiguity Environment NavigationProven track record working effectively in environments with ambiguity, incomplete information, and rapidly changing requirements. Ability to simplify complex, dynamic work and drive execution despite uncertainty.

Skills

Required skills

Technical Project LeadershipExceptional ability to lead cross-functional technical teams through complex delivery roadmaps. Expertise in roadmap planning, dependency management, milestone tracking, and driving execution to completion in high-pressure environments.
AI/LLM Systems KnowledgeDeep technical fluency with large language model systems, prompt engineering, fine-tuning, evaluation methodologies, and common production deployment patterns. Ability to discuss technical details and pressure-test AI architectures.
Customer Engagement and DiscoveryAbility to rapidly embed with customer teams, map complex workflows, identify constraints, and translate business requirements into technical solutions. Strong listening skills and ability to build trust with customer stakeholders.
Strategic Thinking and Pattern RecognitionCapability to step back from execution details and recognize broader trends across multiple deployments. Ability to synthesize field signals into scalable, reusable solutions and connect customer needs to product roadmap priorities.
Executive Communication and PresenceExceptional ability to translate complex technical trade-offs into business language for executive audiences. Skilled at converting strategy into day-to-day technical execution while maintaining stakeholder confidence.
Decision-Making Under PressureDemonstrated judgment and decisiveness in high-stakes situations with incomplete information. Ability to make sequencing decisions, scope trade-offs, and priority adjustments that protect critical path and maintain delivery momentum.

Nice to have

Experience with Large-Scale Infrastructure DeploymentBackground in deploying complex distributed systems, cloud infrastructure, or enterprise platforms at scale. Understanding of deployment pipelines, monitoring, and operational excellence.
Change Management and Adoption LeadershipExperience leading organizational change initiatives, driving user adoption of new systems, and managing the human and organizational dimensions of technology implementation.
Product Management and Strategy ExperienceBackground in product strategy, roadmap planning, and working with research teams to translate insights into product capabilities. Ability to think about market dynamics and customer needs holistically.
Startup or Scaling Organization ExperienceBackground building and scaling high-growth organizations, operating with limited resources, and maintaining execution excellence while managing rapid change and ambiguity.
Multi-language ProficiencyFluency in Swedish or other European languages valuable for the Stockholm-based role and working with European enterprise customers.

Compensation & benefits

Salary

USD 145,000 – 220,000 (annual)

Stock options

Available

Benefits

Equity Participation

Stock options providing ownership stake in OpenAI as the company scales and delivers value. Equity component reflects the significance of this leadership role.

Hybrid Work Model

Stockholm-based role with hybrid arrangement of 3 days in office per week, providing flexibility for remote work and autonomy over schedule while maintaining in-person collaboration.

Relocation Assistance

Comprehensive support for relocating to Stockholm including visa sponsorship support, housing assistance, and relocation logistics to ease transition to the Stockholm office.

International Team Collaboration

Opportunity to work alongside world-class AI researchers, engineers, and customer-focused teams operating at the forefront of AI deployment and product development.

Professional Development

Access to continuous learning opportunities, industry conferences, technical training, and exposure to cutting-edge AI research and deployment practices.

Health and Wellness Benefits

Comprehensive health insurance coverage, mental health support, fitness benefits, and wellness programs reflecting OpenAI's commitment to employee wellbeing.

Competitive Time Off

Generous paid time off, sabbatical programs, and flexible leave policies supporting work-life balance and recovery.

Interview process

  1. 1
    Preliminary Screening Call — Initial conversation with recruiter or hiring manager to discuss background, experience with customer-facing technical leadership, and understanding of AI systems deployment. Assessment of motivation for the role and fit with OpenAI's mission.
  2. 2
    Technical Deep Dive Interview — Conversation with senior engineer or architect covering technical fluency with AI/LLM systems, system design thinking, and ability to discuss architectural trade-offs. May include discussion of past projects and technical decisions made.
  3. 3
    Customer Engagement Scenario — Case study or scenario-based interview evaluating ability to map complex customer workflows, identify constraints, design solutions, and communicate trade-offs. Assessment of customer empathy and discovery skills.
  4. 4
    Project Leadership and Execution — Interview focused on past experience leading complex, high-stakes delivery engagements. Discussion of how you managed ambiguity, made prioritization decisions, coordinated teams, and delivered results under pressure.
  5. 5
    Cross-Functional Collaboration Discussion — Conversation with product manager, researcher, or customer engineer about ability to partner effectively across functions, translate between business and technical contexts, and drive alignment on delivery priorities.
  6. 6
    Leadership Interview — Final discussion with senior leader covering strategic thinking, pattern recognition across deployments, ability to influence roadmap, and vision for how you would approach the role at OpenAI.
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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
LangChainOpenAI APIRAG (Retrieval-Augmented Generation)
Databases
Vector Databases (Pinecone, Weaviate, Milvus)Enterprise Data Warehouses
Tools
Project Management Tools (Jira, Asana, Linear)Git and Version ControlCloud Platforms (AWS, GCP, Azure)API Development and Integration Tools
Other
AI Evaluation Frameworks and MetricsProduction Monitoring and ObservabilityEnterprise Security and Compliance

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