MLOps / AI Operations Engineer
PwC Open · verified Oct 22, 2026 At a glance
Mid-level ML Platform & Ops role at PwC. Bucharest · full-time.
Pay not stated ML Platform & Ops salary
aimlroles summary, based on the employer's posting.
What you'll do
- Build delivery pipelines for AI services, prompts, agent setups, infrastructure and evaluation materials
- Automate environment setup, testing, releases and rollbacks, while capturing evidence for each release
- Set up tracing, logs, alerts and dashboards to track models, agents and operational health
- Manage evaluation thresholds and incident response, then use findings to improve operations
- Track latency, capacity, token consumption and infrastructure costs; prepare runbooks and production handovers
What you bring
- At least four years in DevOps, platform or ML engineering, SRE, or cloud operations
- Strong hands-on skills in automation, containers, cloud services, observability and Infrastructure as Code
- Experience deploying or running ML, generative AI or distributed application workloads
- Working knowledge of release controls, reliability, security and cost optimization
Who this fits
This mid-level role suits an engineer focused on putting AI services into production and keeping them observable, reliable and cost-aware. You’ll also contribute reusable technical assets, support client workshops and proposals, and coach colleagues.
From the employer
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.