MLOps Engineer Job Description

What "MLOps Engineer" means

MLOps Engineers own the infrastructure that AI and ML models run on. The closest analogy in traditional software is a platform or DevOps engineer — but the specific challenges of AI systems (model versioning, inference optimisation, non-deterministic output monitoring, GPU resource management) require specialised knowledge that general DevOps engineers don't have.

Across 41 active listings, MLOps and AI Platform roles are in high demand at companies with mature AI products that need to scale. These roles sit upstream of product features: you're building the serving layer, monitoring systems, and deployment pipelines that allow other engineers to ship AI reliably. LLMs appear in 76% of listings — reflecting that MLOps in 2026 means LLM infrastructure, not just classical ML pipelines.

Required skills

Frequency = % of 41 active listings that mention this skill. Priority is derived from frequency and listing emphasis.

Day-to-day responsibilities

• 1Build and maintain the model serving infrastructure — deploying models to production, managing scaling, ensuring reliability
• 2Own CI/CD pipelines for ML models: automated testing, versioning, rollback, blue-green deployments
• 3Implement monitoring for AI systems: output quality metrics, latency tracking, cost per inference, drift detection
• 4Manage GPU compute resources — allocation, scheduling, utilisation optimisation
• 5Build internal platforms that allow ML Engineers and AI Engineers to deploy models without infrastructure expertise
• 6Set up experiment tracking and reproducibility infrastructure (MLflow, W&B, or custom tooling)
• 7Design and operate vector database infrastructure for RAG systems at scale

How it differs from related roles