MLOps Engineer

About the Role

We are looking for an MLOps Engineer with 2 to 5 years of experience to build and maintain the infrastructure, pipelines, and tooling that take machine learning models from development to production reliably. You will work closely with data scientists and AI engineers to remove the friction between model building and model deployment.

In This Role, You Will

• Build and maintain CI/CD pipelines for model training, evaluation, and deployment.
• Design and manage model serving infrastructure for low-latency, high-reliability inference at scale.
• Build data and feature pipelines that feed model training and inference systems reliably.
• Implement model monitoring systems to detect performance drift, data drift, and unexpected behaviours in production.
• Manage experiment tracking and model versioning using tools like MLflow or Weights and Biases.
• Containerise ML workloads using Docker and orchestrate them on Kubernetes or similar platforms.
• Optimise inference costs and latency through batching, quantisation, and hardware-aware deployments.
• Work with data scientists to ensure models are production-ready in terms of quality, reliability, and observability.

You Might Thrive in This Role If You

• Have 2 to 5 years of MLOps or ML infrastructure engineering experience.
• Are proficient in Python and comfortable with both ML frameworks and DevOps tooling.
• Have hands-on experience with cloud ML platforms on AWS SageMaker, GCP Vertex AI, or Azure ML.
• Understand the full ML lifecycle and the specific reliability challenges of production ML systems.
• Can work closely with data scientists while also bringing strong engineering rigour to the infrastructure.

Bonus If You Have

Experience with LLM serving infrastructure and optimising transformer model inference.
Familiarity with feature stores such as Feast or Tecton.
Knowledge of distributed training frameworks such as Ray or Horovod.
Experience with Kubeflow, Airflow, or Prefect for pipeline orchestration.

How to Apply

Send your resume and a short note about an ML system you built and the reliability or performance challenges you solved.

Email us at [email protected] with the subject line MLOps Engineer Application.