AI Platform Reliability Engineer

Ubisoft; Saint-Mandé; In loco; EUR 90.000 - 150.000; Tempo pieno

Vantaggi offerti da questo lavoro

Profit Sharing

Company savings plan

Paid time off 25 days + 12 extra days

Transport pass 50% paid by company

Lunch vouchers 9€/day

Healthcare for you and your family

Office in Saint Mandé with gym

Descrizione del lavoro

Ubisoft is redefining how teams operate AI-powered experiences in Saint-Mandé, combining reliability engineering with scalable AI platform practices. You will own the AI gateway, MCP servers, and LLM serving infrastructure, ensuring observability, resilience, and cost efficiency while aligning with security and product needs.

You will collaborate across software, data, ML, and security teams to productionize AI services, design deployment patterns, and drive reliability improvements in a

Competenze

• 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.

Mansioni

• 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.

Conoscenze

SRE / Platform engineering

Backend engineering

Reliability practices

Observability & monitoring

Python

Rust exposure / willingness

Cloud platforms

Docker

Kubernetes

CI/CD pipelines

Infrastructure as Code

Distributed systems

Microservices & APIs

Networking fundamentals

Production debugging

Cross-functional collaboration

Strumenti

GitLab

Docker

Kubernetes

Infrastructure as Code

CI/CD

Descrizione del lavoro

Ubisoft is redefining how teams operate AI-powered experiences in Saint-Mandé, combining reliability engineering with scalable AI platform practices. You will own the AI gateway, MCP servers, and LLM serving infrastructure, ensuring observability, resilience, and cost efficiency while aligning with security and product needs.

You will collaborate across software, data, ML, and security teams to productionize AI services, design deployment patterns, and drive reliability improvements in a