We’re looking for a hands-on DevOps / Platform Engineering Lead who is not afraid to take ownership, drive change, and work across multiple engineering teams.
This is a role for someone who can go beyond “keeping things running” — someone who enjoys building platforms, defining engineering standards, solving complex problems, and helping development teams become more autonomous and efficient.
What you’ll do
- Develop our internal developer platform, including self-service capabilities, golden paths, and repository templates, with GitHub as the center of our engineering ecosystem
- Own the administration and support the migration to GitHub across the organization
- Design and maintain CI/CD with GitHub Actions, including reusable workflows, self-hosted runners, security policies, and secrets management
- Build and maintain Infrastructure as Code with Terraform, including reusable modules, versioning, state management, multi-environment setups, and Policy as Code
- Build and operate the AI model access layer, including gateways, routing, rate limits, and usage observability
- Drive AI cost modeling and optimization, covering input/output pricing, prompt caching, Batch APIs, model selection, and unit economics per feature/user
- Establish AI cost reporting for the business, including showback/chargeback, budgets, and alerts
- Work with multiple engineering teams to define and promote best practices, reusable patterns, and engineering standards
- Mentor engineers and act as a technical partner for architects and product teams
- Take ownership of initiatives end-to-end and make sure solutions are actually adopted across the organization
What we’re looking for
- 5+ years of experience in DevOps, SRE, or Platform Engineering, including experience as a Tech Lead or team lead
- Strong knowledge of GitHub at organizational/enterprise scale, including GitHub Actions, GitHub Enterprise, permissions, governance, and GitHub Advanced Security
- Production-level Terraform experience — building reusable modules, refactoring state, and managing multiple environments
- Hands‑on experience with AI model APIs and tooling, such as Anthropic, OpenAI, AWS Bedrock, Google Vertex AI, or similar
- Good understanding of AI cost models: tokens, context windows, caching, pricing tiers, GPU vs. API costs, and quality/cost/latency trade‑offs
- Experience with Kubernetes, containerization, and cloud platforms such as AWS, GCP, or Azure
- Ability to communicate about technology and costs with both engineers and finance/business stakeholders
- Strong ownership mindset — you take responsibility, make decisions, and drive things to completion
- Ability to work effectively with a large number of engineering teams, understand their needs, and turn them into scalable platform solutions and practical best practices
- Strong communication and collaboration skills, with the ability to influence teams without relying solely on formal authority
Nice to have
- Experience with Microsoft Azure
- Experience with Jenkins
- Experience working with enterprise-scale environments
- Experience building internal developer platforms or platform engineering capabilities
What we value
We’re looking for someone who is pragmatic, proactive, and technically strong.
Someone who doesn’t wait for requirements to be handed over, but can identify opportunities, propose solutions, bring people together, and take ownership from idea → implementation → adoption.
You’ll have a real opportunity to influence how engineering teams work, how infrastructure is delivered, and how we build and scale our AI capabilities in a secure, observable, and cost-efficient way.