ML Engineer (MLOps)

About the role

An experienced ML Engineer (MLOps) is sought to take machine learning models and AI pipelines from proof of concept to scalable, reliable production deployment. The role covers deploying, monitoring and optimizing ML solutions across cloud and edge environments, working closely with Data Scientists, Software Engineers, DevOps teams and Product Managers.

Key responsibilities

• Deploy ML models and AI pipelines from development and proof of concept into production, with CI/CD integration and orchestration.
• Implement monitoring, evaluation and maintenance strategies to keep model accuracy and performance in production.
• Optimize models for inference speed and resource efficiency (quantization, pruning, knowledge distillation) for edge and cloud.
• Collect, clean, preprocess data and engineer features; build continuous or semi-continuous training and evaluation workflows.
• Contribute to automated ML pipelines, model lifecycle management and reproducible workflows with MLOps tools.
• Work with cross-functional teams to turn business and technical requirements into reliable ML solutions.
• Maintain documentation of code, models, deployment workflows and operations.

Required profile

• Bachelor's or Master's degree in Computer Science, Machine Learning, Data Science, AI or a related field.
• 5-9 years of professional experience in machine learning engineering or related roles.
• Strong hands-on experience deploying and operating ML models in production.

Required skills

• Python and ML libraries such as PyTorch, NumPy, Pandas and Scikit-learn.
• Docker and Kubernetes; SQL and NoSQL databases.
• At least one cloud platform: AWS, Azure or GCP.
• MLOps tools such as MLflow, ClearML, Azure Machine Learning or AWS SageMaker.
• Deep learning, reinforcement learning and other ML techniques.
• Preferred: edge deployment experience, infrastructure-as-code and observability tools, model optimization, GPU acceleration, distributed computing, and open-source contributions.