MLOps / AI Operations Engineer

Description

The team industrializes AI delivery, covering automated deployment, evaluation work, observability, reliability engineering and clear control of consumption.

You join the practice focused on AI transformation and agentic systems, working on client projects.

Success is judged by how often releases ship and succeed, how quickly incidents are detected and restored, how much of the system is evaluated and monitored, reliability and latency, and how transparent cost and consumption are.

You work alongside practice colleagues and the company's sector, functional, cloud, cyber, risk, Responsible AI and change specialists, plus client stakeholders such as business owners, product owners, technology teams and operators, and alliance or implementation partners where relevant.

Responsibilities

• Create CI/CD pipelines covering AI services, prompts, agent configurations, infrastructure and evaluation assets.
• Automate environment provisioning, testing, deployment, rollback and the creation of release evidence.
• Set up tracing, logging, monitoring of models and agents, alerting and operational dashboards.
• Turn evaluation thresholds, incident handling and continuous improvement loops into everyday operations.
• Track latency, capacity, token use, infrastructure consumption and the main cost drivers.
• Write runbooks, define service ownership and hand production support over.
• Contribute to proposals, client workshops and market development, scaled to your seniority.
• Share reusable methods, patterns, code, assets and lessons learned.
• Coach colleagues and take part in the capability's learning plan.
• Follow the company's quality, independence, confidentiality and risk management requirements.

Requirements

• You bring 4+ years working in DevOps, platform engineering, ML engineering, SRE or cloud operations.
• Solid skill in automation, containers, cloud services, observability and Infrastructure as Code.
• A track record deploying or running ML, generative AI or distributed applications.
• Working knowledge of release controls, reliability, security and cost optimization.
• Hands-on use of GitHub Actions, Azure DevOps, GitLab CI or similar, and IaC with Terraform, Bicep or comparable tools.
• Strong Docker and Kubernetes skills, plus experience deploying AI or agent services in cloud and hybrid setups.
• Experience managing prompts, agent configurations, evaluation datasets and release evidence with MLflow, platform-native registries or similar lifecycle tools.
• Practical agent tracing and observability work with OpenTelemetry and tools such as LangSmith, MLflow, Langfuse, Azure Monitor, Prometheus or Grafana.
• Able to watch model and agent quality, tool failures, retrieval performance, latency, token use, cost, capacity and workflow-level service signals.
• Experience with progressive delivery, rollback, secrets management, vulnerability scanning, incident response and reliability practice for non-deterministic AI systems.

Conditions

Hybrid work arrangement.

Requisition tags: LI-BS1, LI-Hybrid.