Experis is seeking an experienced MLOps Lead to join a high-profile consulting engagement with a major client in London. This is a fantastic opportunity to lead the design, implementation, and optimization of machine learning operations in a dynamic, multi-cloud environment.
Key Responsibilities:
• Lead the end-to-end deployment and operationalization of machine learning models in production environments.
• Design and implement scalable MLOps pipelines, ensuring robust CI/CD practices for ML workflows.
• Collaborate closely with Data Scientists and Engineering teams to streamline model lifecycle management.
• Drive best practices for monitoring, versioning, and governance of ML models.
• Advise clients on MLOps strategy and architecture, leveraging your consulting expertise.
• Ensure compliance with security, performance, and reliability standards across multi-cloud platforms.
Required Skills & Experience:
• Data Science Background: Strong foundation in data science principles, statistical modeling, and machine learning algorithms.
• Consulting Experience: Proven track record of delivering solutions in a client-facing consulting environment.
• MLOps Expertise: Hands-on experience with tools such as MLflow, Kubeflow, TensorFlow Serving, or similar.
• Cloud Platforms: Proficiency in multi-cloud environments (AWS, Azure, GCP).
• DevOps & Automation: Strong knowledge of CI/CD pipelines, containerization (Docker, Kubernetes), and infrastructure as code (Terraform).
• Programming: Advanced skills in Python and familiarity with other languages (e.g., R, Java).
• Academic Background: Degree in Computer Science, Data Science, or related discipline (Master's or PhD preferred).
Preferred Qualifications:
• Experience with large-scale data systems and distributed computing.
• Familiarity with compliance frameworks and data governance in regulated industries.
• Excellent communication and stakeholder man
