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 OpenTelemetry and tools such as LangSmith, 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