MLOps Engineer

Most AI projects stop before production. Your job is to make sure they get there, and stay there. At Diametral, the MLOps Engineer designs the platforms and pipelines that make it possible to deploy, monitor and evolve machine learning models and LLMs reliably.

You join the Architecture & Engineering engine and work hand in hand with Data Scientists and GenAI Engineers, from Build through Run.

What you will build

• MLOps and LLMOps platforms: training, model registry, deployment, inference at scale.
• CI/CD/CT pipelines for models: testing, validation, automated release, rollback.
• Production model monitoring: performance, data and model drift, alerting, explainability.
• Infrastructure as code and reproducible environments, on Kubernetes and in the cloud.
• Technical AI governance: traceability, versioning, access management, compliance elements.
• Infrastructure and inference cost optimization (FinOps, GPUs, serving).

Your tech stack

Python, Docker, Kubernetes, Terraform, MLflow, Kubeflow or Vertex AI Pipelines, Azure ML, SageMaker, Databricks, Airflow, Prometheus, Grafana, GitLab CI or GitHub Actions, serving tools (vLLM, Triton, KServe).

Within Diametral

• You contribute to our reference MLOps foundations, reused from mission to mission.
• You spread production best practices across the whole data and AI community.

What you bring

• An engineering degree or a master's in computer science.
• At least 3 years of experience in MLOps, DevOps, platform engineering or ML Engineering.
• Solid command of Python, containers, Kubernetes and at least one public cloud.
• Hands-on experience deploying and monitoring models in production.
• Fluent English in a client context, plus the language of your home office.

What will make the difference

• LLMOps experience: LLM serving, continuous evaluation, agent observability.
• Cloud or Kubernetes certifications.
• A FinOps and security mindset.

Your mindset

• Reliability first: you build systems that can be diagnosed when they fail.
• Automation by default.
• A sense of service toward the teams you equip.

What we expect from all our talent

• An obsession with business impact: you quickly understand our clients' business, and you know how to challenge it.
• A strong sense of service and client experience.
• Top-level communication, written and spoken: you simplify, you bring people along.
• Advanced mastery of AI and automation tools: you can show us an agent or a prototype you built yourself.
• Ambition and rigor, serving a team that puts the collective before the individual.

• Interview with the Talent Acquisition team and/or a Business Manager
• Technical interview with an engine expert
• Interview with the Managing Director or the Engine Director