Remote MLOps Engineer — AI Training Pipelines & Scale

ixolabs.ai; United Kingdom; Remote; GBP 96,000 - 165,000; Part time

Benefits offered by this job

Flexible hours

Stripe/bank payment cadence

Job summary

ixolabs.ai is seeking an MLOps Engineer for AI training to build scalable pipelines, registries, and rollback plans. You will review code across Kubeflow, MLflow, SageMaker, Vertex AI & Azure ML and improve CI/CD for ML workflows.

Ideal candidates bring multiple cloud ML platform strengths, Docker/Helm proficiency, and experience with feature stores and model registries. Remote, flexible hours, contract-based engagement.

Qualifications

• 5+ years in MLOps, ML platform engineering, or production ML systems.
• Strong familiarity with Kubernetes-based ML serving and at least one major cloud ML platform.
• Solid understanding of Docker, Helm, and infrastructure-as-code for ML workloads.

Responsibilities

• Generate and evaluate instruction-response pairs covering ML pipelines, feature stores, and model registries.
• Review AI-generated code using Kubeflow, MLflow, SageMaker, Vertex AI, and Azure ML.
• Provide feedback on CI/CD for ML, model packaging, and reproducibility.
• Validate AI handling of model serving (Triton, TorchServe, BentoML, KServe) and autoscaling.
• Evaluate observability for ML (Evidently, WhyLabs, custom monitoring) and drift detection.
• Identify subtle issues in pipeline DAGs, feature/serving skew, and shadow deployments.

Skills

MLOps experience

Kubernetes serving

Docker/Helm

Feature stores

Python

Systems language

Tools

Kubeflow

MLflow

SageMaker

Vertex AI

Azure ML

Job description

ixolabs.ai is seeking an MLOps Engineer for AI training to build scalable pipelines, registries, and rollback plans. You will review code across Kubeflow, MLflow, SageMaker, Vertex AI & Azure ML and improve CI/CD for ML workflows.

Ideal candidates bring multiple cloud ML platform strengths, Docker/Helm proficiency, and experience with feature stores and model registries. Remote, flexible hours, contract-based engagement.