AI Engineer
Posted Updated
Summary
AI Engineer at Oxylabs (Vilnius) owning internal AI services end-to-end — from design to production — that boost developer productivity for a 60-person engineering team. Day to day involves building and maintaining AI integrations with Python/Go, managing Docker/CI-CD deployments on Kubernetes, and ensuring strong observability.
We’re a team of 500+ professionals who develop cutting-edge proxy and web data scraping solutions for thousands of the world’s best known businesses, including Fortune 500 companies.
What’s in store for you:
You’ll be solving complex challenges and maintaining our own infrastructure with 60PB+ monthly data traffic. Here are its scale and maturity in numbers:
- 6PB+ Ceph storage
- 60PB+ monthly data traffic through our systems
- 300k+ service requests/sec processed
- 500k+ Kafka messages/sec streamed
The team waiting for you:
The AI Integrations squad builds critical and quality-of-life AI services that support a wider team of 60 developers. As an AI Engineer, you will take full ownership of problem statements - from initial design to final production - building solutions that tangibly improve our teams' daily productivity. We are currently shifting gears from maintaining our initial successful services to creating bigger, more impactful internal tools. The core challenge is navigating fast-changing AI tooling to create pragmatic, functional, and highly observable solutions that developers actively adopt.
In this role, you will:
- Maintain and optimize our existing AI services, ensuring high observability and reliability.
- Investigate, design, and implement new internal AI services aimed at increasing developer productivity.
- Take end-to-end ownership of your services' lifecycles, managing resources, Docker containers, and CI/CD deployments.
- Partner directly with other squads to understand their bottlenecks and build tailored, AI-based solutions.
- Upgrade our incident context-gathering service to automatically determine the correct rotation to call during incidents.
- Develop automations and plugins for Spotify Backstage to make our internal documentation platform more effective.
Your skills & experiences:
- Solid production experience coding in Python or Go.
- Hands-on experience with AI integrations, including working with APIs, prompt engineering, and hybrid deterministic/AI logic.
- Proven ability to take full service ownership, independently managing deployments and CI/CD pipelines.
- Experience implementing comprehensive observability for your services using metrics, logging, and SLIs.
- A proactive and pragmatic mindset, comfortable engaging directly with developers and adapting to fast-changing AI tooling.
Nice to Have:
- Experience in DevEx (Developer Experience) tool development.
- Familiarity with incident platform integrations (e.g., incident.io).
- Previous experience building plugins for Spotify Backstage.
- Experience with self-hosted model deployments.
Tech stack:
- Python
- Go
- Kubernetes
- ArgoCD
- Docker
- GitLab
- Ansible
- Terraform
- Vault
- Prometheus
- ELK
Salary & Benefits:
- Gross salary: 2800 - 5600 EUR/month. Keep in mind that we are open to discuss a different salary based on your skills and competencies.
- Growth & Learning: 40+ internal learning options, external conferences, mentorship, and year-round knowledge-sharing.
- Health & Well-being: Private health insurance, psychotherapy, on-site well-being consultants, 24/7 gym access, and a wellness app.
- Celebration & Community: Team events, an overseas workation, quarterly team-building budgets, and plenty of ways to mark milestones together.
Up for the challenge? Let’s talk!
Up for the challenge? Let’s talk!
Skills
As published by lever · 4 questions · 4 written answers
Resume/CV, Full name, Email, Phone, Current location, Current company, Link to your profile (Linkedin/Github etc.) URL
- Would you be able to work at least 3 days per week at the Vilnius office?
- Which programming language are you most comfortable using in a production environment: Python, Go, or both? optional
- Please briefly name or describe one AI integration or tool you’ve built (e.g., using LLM APIs). optional
- Which tools do you typically use to deploy and monitor your own code (e.g., Docker, CI/CD, Prometheus, etc.)? optional