MLOps / AI Operations Engineer

PwC is hiring a MLOps / AI Operations Engineer in Bucharest, Romania. Level rates it ; you can apply on PwC.

Level's read on this job

Builds AI ●●●● is how Level rates this role: the job is building AI systems.
The posting mentions MLflow, GitHub Actions and Azure DevOps among its tools and skills.
Level classes it as a senior role.
PwC has 3,700+ other open roles on Level.

AI in this role

Industrialize AI delivery through automated MLOps pipelines, evaluation operations, and observability for agentic services.

MLflowGitHub ActionsAzure DevOpsGitLabTerraformBicepDockerKubernetesOpentelemetryLangsmithLangfusePrometheus+1

ml-opsai-safetydevopsmlopssreci-cdobservabilityinfrastructure-as-code

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

How we rate this

MLOps / AI Operations Engineer at PwC rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Key facts

MLOps / AI Operations Engineer is an open job at PwC.
Level rates this role AI Level 4 of 4: Builds AI.
The role is based in Bucharest.
The job was posted on September 23, 2026.
Seniority: senior.
Applications are handled by PwC. Apply on PwC

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

ML OpsAI SafetyDevOpsMLOpsSreCI/CDObservabilityInfrastructure As Code

Questions you could be asked

How do you monitor a model once it's live, and how do you know it needs retraining?
How do you think about the risk of an AI system in this kind of role failing silently?
Tell me about a project where devops was part of your work. What did you do?
Tell me about a project where mlops was part of your work. What did you do?
Tell me about a project where sre was part of your work. What did you do?

Adapt your resume

List these exact terms on your resume: ML Ops, AI Safety, DevOps, MLOps, and Sre. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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