Senior MLOps Engineer

About the role

An experienced Senior MLOps Engineer is sought to build, deploy, scale and maintain machine learning solutions in production. The role covers machine learning infrastructure, cloud platforms, automation, CI/CD pipelines and model lifecycle management.

Key responsibilities

• Design, build and maintain scalable MLOps pipelines for end-to-end machine learning workflows.
• Develop and manage CI/CD/CT pipelines for machine learning models.
• Automate model training, validation, deployment, monitoring and retraining.
• Build and maintain ML infrastructure on AWS, Azure or GCP.
• Implement model versioning, experiment tracking and model registry solutions.
• Monitor model performance, data quality, model drift and system reliability.
• Collaborate with Data Scientists, ML Engineers, Data Engineers and DevOps teams.
• Optimize ML workloads for performance, scalability, cost and reliability.
• Use Docker and Kubernetes for containerization and orchestration.
• Establish best practices for model governance, security and reproducibility.

Required profile

• 5+ years of experience in MLOps, DevOps, Machine Learning Engineering or a related field.
• Strong programming skills in Python.
• Hands-on experience with TensorFlow, PyTorch or Scikit-learn.
• Experience building CI/CD pipelines with GitLab CI, Jenkins or GitHub Actions.
• Practical experience with MLflow, Kubeflow, Airflow or Prefect.
• Experience with AWS, Azure or GCP; familiarity with Terraform.
• Solid understanding of Linux, networking, APIs and distributed systems.

What we offer

• Good to have: LLMs and generative AI deployment, LLMOps, RAG pipelines, vector databases, GPU infrastructure, distributed training, feature stores and real-time inference.