Senior Machine Learning Engineer

Accountabilities • Drive and continuously improve MLOps practices across the machine learning environment.

• Build, optimize, and maintain CI/CD pipelines using GitLab to automate reliable ML delivery.

• Implement and maintain experiment tracking and model management workflows using MLflow.

• Productionize, deploy, and maintain machine learning models using AWS, with a strong focus on SageMaker.

• Design, build, and maintain scalable machine learning and data pipelines.

• Develop and maintain robust Python-based ML and data infrastructure.

• Implement monitoring, observability, and operational practices to ensure ML systems remain reliable and performant.

• Apply software engineering best practices, including automated testing, documentation, version control, and system design.

• Provide technical guidance and mentorship to Data Scientists, Data Engineers, and MLOps Engineers.

• Collaborate closely with Product Managers, engineers, data professionals, and business stakeholders to align technical solutions with business objectives.

• Evaluate emerging technologies, tools, and methodologies that can improve machine learning capabilities and operational efficiency.

• Contribute to the continuous improvement of the ML platform and its ability to support scalable production workloads.

Requirements

• 5+ years of professional experience in Machine Learning Engineering or a closely related field.

• Strong hands-on experience deploying, operating, and maintaining production machine learning systems.

• Expert-level Python skills and strong knowledge of the broader data science and machine learning ecosystem.

• Hands-on experience with AWS cloud services, preferably including AWS SageMaker.

• Strong understanding of MLOps principles, practices, tooling, and the machine learning lifecycle.

• Practical experience with MLflow for experiment tracking and model management.

• Experience designing and maintaining GitLab CI/CD pipelines.

• Hands-on experience with at least one major deep learning framework, such as PyTorch or TensorFlow.

• Proven experience designing and building scalable ML and data pipelines.

• Experience implementing monitoring and observability for machine learning systems.

• Ability to design, document, explain, and communicate complex technical architectures to both technical and non-technical stakeholders.

• Experience mentoring engineers and data scientists and providing technical leadership.

• Strong communication, collaboration, and stakeholder management skills.

• Bachelor's degree in Computer Science, Engineering, or a related technical discipline, or equivalent practical experience.

• Master's degree or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field is a plus.

• Experience with Prometheus, Grafana, Evidently AI, or similar monitoring and observability technologies is preferred.

• Experience working with large-scale recommender systems is highly valued.

• Strong understanding of software engineering principles, architecture, and system design is preferred.

Benefits

• B2B contract arrangement.

• Opportunity to work on technically challenging machine learning projects with mature engineering practices.

• Exposure to modern ML technologies, AWS infrastructure, MLOps tooling, and enterprise-scale systems.

• Opportunity to contribute to a globally deployed recommender system and production ML platform.

• Collaborative and supportive environment focused on knowledge sharing and professional development.

• Opportunity to provide technical mentorship and influence engineering practices across multidisciplinary teams.

• Exposure to complex machine learning infrastructure, automation, observability, and scalable system design.

• Opportunity to evaluate and introduce new technologies that improve ML capabilities and operational efficiency.

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