MLOps / AI Operations Engineer

Job Description & Summary The opportunity Industrialize AI delivery through automated deployment, evaluation operations, observability, reliability engineering and transparent consumption management. What you will be doing

• Build CI/CD pipelines for AI services, prompts, agent configurations, infrastructure and evaluation assets.
• Automate environment provisioning, testing, deployment, rollback and release evidence.
• Implement tracing, logging, model and agent monitoring, alerts and operational dashboards.
• Operationalize evaluation thresholds, incident handling and continuous-improvement loops.
• Monitor latency, capacity, token usage, infrastructure consumption and cost drivers.
• Define runbooks, service ownership and production support handover. What we need from you
• 4+ years in DevOps, platform engineering, ML engineering, SRE or cloud operations.
• Strong automation, containers, cloud services, observability and Infrastructure as Code capability.
• Experience deploying or operating ML, generative AI or distributed application workloads.
• Understanding of release controls, reliability, security and cost optimization. Relevant AI technologies and tooling
• Hands-on experience with GitHub Actions, Azure DevOps, GitLab CI or equivalent, plus Infrastructure as Code using Terraform, Bicep or comparable tooling.
• Strong container and orchestration capability using Docker and Kubernetes, together with experience deploying AI or agent services across cloud and hybrid environments.
• Experience operating model and prompt assets, agent configurations, evaluation datasets and release evidence using MLflow, platform-native registries or equivalent lifecycle tooling.
• Practical implementation of agent tracing and observability using Open. Telemetry and tools such as Lang. Smith, MLflow, Langfuse, Azure Monitor, Prometheus or Grafana.
• Ability to monitor model and agent quality, tool failures, retrieval performance, latency, token usage, cost, capacity and workflow-level service indicators.
• Experience with progressive delivery, rollback, secrets management, vulnerability scanning, incident response and reliability practices for non-deterministic AI systems. Measures of success
• Deployment frequency and success rate
• Mean time to detect and restore
• Evaluation and monitoring coverage
• Service reliability and latency
• Cost and consumption transparency Key interfaces
• Other members of the AI Transformation & Agentic Systems Practice
• PwC sector, functional, cloud, cyber, risk, Responsible AI and change specialists
• Client business owners, product owners, technology teams and operational users
• Technology alliance and implementation partners where relevant Contribution to the practice
• Support proposals, client workshops and market development appropriate to seniority.
• Contribute reusable methods, patterns, code, assets and lessons learned.
• Coach colleagues and participate in the capability’s continuous learning agenda.
• Uphold PwC quality, independence, confidentiality and risk-management requirements. #LI-BS1 #LI-Hybrid