Site Reliability Engineer – AI Applications (M/W/X)
Ubisoft is hiring a Site Reliability Engineer – AI Applications (M/W/X). Level rates it ; you can apply on Ubisoft.
Level's read on this job
- Builds AI ●●●● is how Level rates this role: the job is building AI systems.
- Tools and skills the posting cites include RAG, AI agents and AI safety.
- Level lists 326 other open roles at Ubisoft.
- The role was posted within the past week.
AI in this role
Ubisoft is a global leader in gaming with teams across the world creating original and memorable gaming experiences, from Assassin’s Creed, Rainbow Six, to Just Dance and more. We believe diverse perspectives help both players and teams thrive. If you’re passionate about innovation and pushing entertainment boundaries, join our journey and help us create the unknown!
At Ubisoft, we are reshaping how teams work with information through AI. We build and operate applications for document-rich, interactive, and intelligent use cases, including hybrid search, question answering, document generation, conversational assistants, agentic workflows, and coding assistants.
You will join the team responsible for the reliability, scalability, and operational excellence of the AI platform powering these experiences. This includes the AI Gateway, MCP servers, LLM serving infrastructure, retrieval services, and the surrounding platform components that help teams bring AI applications to production safely.
This role sits at the intersection of fast-moving AI engineering and disciplined site reliability engineering. You will help the platform evolve quickly while ensuring it remains observable, resilient, cost-efficient, and dependable at scale.
Responsibilities
- Operate and improve the reliability, scalability, and performance of the AI platform, including the AI Gateway, MCP servers, LLM serving, retrieval services, and related platform components.
- Define and maintain SLOs, SLIs, and error budgets to guide reliability decisions and balance delivery speed with platform stability.
- Build strong observability across infrastructure, services, and AI-specific signals, such as latency, token throughput, model/provider errors, cost, saturation, and quality indicators.
- Improve the performance, efficiency, and cost profile of model and API serving across cloud, containerized, and high-throughput environments.
- Design graceful degradation, fallback, and failover strategies, including model, provider, and region-level resilience patterns.
- Maintain safe, repeatable deployment practices using GitLab CI/CD, infrastructure as code, automated testing, and progressive delivery where appropriate.
- Lead or contribute to incident response, post-incident reviews, capacity planning, and reliability improvements.
- Partner closely with software engineers, data scientists, ML engineers, security teams, and product stakeholders to productionize AI services responsibly.
- Help establish operational standards, runbooks, dashboards, and best practices for AI application reliability.
Must-Have
- Proven experience in SRE, platform engineering, backend engineering, or infrastructure roles in compute-intensive, data-intensive, or distributed environments.
- Strong understanding of reliability engineering practices, including SLOs, error budgets, observability, capacity planning, incident management, and post-incident analysis.
- Strong programming skills in Python and practical experience with Rust, or a willingness to work deeply with Rust-based services.
- Hands-on experience with cloud platforms, Docker, Kubernetes, and production-grade service operations.
- Experience building and maintaining CI/CD pipelines, preferably with GitLab, and working with infrastructure as code.
- Solid understanding of distributed systems, microservices, APIs, networking fundamentals, and production debugging.
- Ability to move quickly in a fast-changing AI ecosystem while applying the rigor, discipline, and risk awareness expected from an SRE.
- Clear communication skills and the ability to collaborate across engineering, data, ML, security, and product teams.
Nice-to-Have
- Experience operating AI infrastructure, such as AI gateways, MCP servers, LLM serving stacks, model routers, or provider abstraction layers.
- Familiarity with RAG and agentic architectures, including embeddings, vector databases, hybrid search, query processing, tool use, and agent orchestration.
- Experience with GPU-backed inference, high-throughput serving, batching, caching, autoscaling, or model performance optimization.
- Experience with serverless, event-driven systems, queues, streaming platforms, or asynchronous processing.
- Familiarity with AI safety, governance, auditability, data privacy, or secure-by-design platform practices.
- Open-source contributions, technical writing, talks, or publications in relevant engineering, reliability, or AI infrastructure fields.
You will work on projects that shape the future of AI at Ubisoft. You will stay close to emerging AI infrastructure and reliability practices, influence how we operate AI platforms at scale, and help teams deliver useful, safe, and dependable AI-powered applications.
Ubisoft's perks
💰 Profit Sharing, yearly company saving plan. 25 paid time off + 12 additional paid days off. 50% of your transportation pass is paid by the company, lunch vouchers (9€/day), healthcare for you and your family, and lots of Ubisoft additional perks.
👶 Maternity leaves of 20 weeks, paternity/co-parental leaves of 7 weeks.
📍 Our office is located in Saint Mandé, (Metro line 1, Saint Mandé station). Gym available in the building.
Information about Ubisoft
Ubisoft offers the same job opportunities to all, without any distinction of gender, ethnicity, religion, sexual orientation, social status, disability, or age. Ubisoft ensures the development of an inclusive work environment which mirrors the diversity of our gamers’ community.
How we rate this
Site Reliability Engineer – AI Applications (M/W/X) at Ubisoft rates 94 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- How would you design a retrieval step so the model answers from real data instead of guessing?
- How do you decide when an AI agent can act on its own versus asking for approval first?
- How do you think about the risk of an AI system in this kind of role failing silently?
- How would you decide a model or AI system is ready to ship?
- Tell me about a time a model underperformed in production. How did you find out, and what did you change?
Adapt your resume
- List these exact terms on your resume: RAG, AI agents, and AI Safety. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
Want an expert to read your CV for this job?
Free. Send your CV and the role you want next. We reply by email within 2 to 4 business days.
Get new software engineering jobs (Builds AI ●●●●) by email
One email a week with the new software engineering jobs (Builds AI ●●●●), each rated for how much AI is in the work. No recruiter spam, unsubscribe in one click.
Free. One email a week. Unsubscribe in one click.
Similar roles
Software Engineering roles that build AI, at other companies.
What kind of AI work fits you?
Answer 12 practical questions in about three minutes. Get a simple profile, the work it points to, and live roles to explore next.
Find my next step