Head of Machine Learning

Head of ML OPS | AI & AUTOMATION CONSULTANCY

Job Title: Head of MLOps Engineering

Location: New York/ US (Open to EU too)

Type: Full Time

Reports To: [Chief Technology Officer (CTO)

Department: Engineering / Machine Learning / AI

Job Overview:

We are seeking an experienced and innovative Head of MLOps Engineering to lead and shape our Machine Learning Operations (MLOps) team. In this role, you will be responsible for building and scaling our MLOps capabilities, driving best practices for model deployment, monitoring, and management, and collaborating with data scientists and engineers to ensure a seamless integration of machine learning models into production. The ideal candidate has a strong background in software engineering, cloud infrastructure, and the deployment of machine learning models at scale.

Key Responsibilities:

Leadership & Strategy:

• Lead and mentor the MLOps team, fostering a culture of collaboration, innovation, and continuous improvement.
• Define and implement the strategic roadmap for MLOps, aligning with overall company objectives and product goals.
• Collaborate closely with data science, engineering, and product teams to understand and address their needs, ensuring that MLOps processes support agile model development and deployment.

MLOps Architecture & Infrastructure:

• Design and build scalable, reliable, and secure MLOps pipelines and infrastructure to support the end-to-end lifecycle of machine learning models.
• Implement best practices for CI/CD in the context of machine learning, including automated testing, model versioning, and deployment.
• Optimize and manage cloud and on-premise infrastructure for ML model training, deployment, and monitoring (e.g., AWS, GCP, Azure, Kubernetes).

Model Deployment & Monitoring:

• Develop frameworks for deploying machine learning models to production environments, ensuring low latency, high availability, and scalability.
• Implement robust monitoring and alerting systems for model performance and data drift, ensuring models continue to deliver value after deployment.
• Work with data engineering teams to ensure data pipelines are efficient, reliable, and aligned with MLOps processes.

Process Optimization & Automation:

• Drive automation across the MLOps lifecycle, including data preprocessing, model training, and model validation.
• Continuously evaluate and integrate new MLOps tools and frameworks to improve productivity, speed of deployment, and quality of models.
• Identify bottlenecks in the ML lifecycle and implement solutions to optimize model training, testing, and deployment times.

Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related field; Ph.D. is a plus.
• 8+ years of experience in software engineering, data engineering, or machine learning roles, with at least 3 years in a leadership position.
• Strong experience with cloud platforms (e.g., AWS, GCP, Azure) and container orchestration systems like Kubernetes.
• Proficiency in Python, SQL, and at least one other programming language (e.g., Java, C++).
• Deep understanding of the machine learning lifecycle, including data preprocessing, model training, deployment, and monitoring.
• Experience with MLOps tools such as MLflow, Kubeflow, Airflow, or similar.
• Knowledge of CI/CD tools and practices for ML workflows (e.g., Jenkins, GitOps).

Skills & Competencies:

• Leadership & Communication: Strong leadership skills with the ability to inspire and guide a team, while effectively communicating complex technical concepts to non-technical stakeholders.
• Problem-Solving: Exceptional analytical and problem-solving abilities, with a data-driven approach to decision-making.
• Innovation & Adaptability: Ability to stay up-to-date with the latest trends and best practices in MLOps and adapt to new tools and methodologies.
• Collaboration: Proven track record of working closely with cross-functional teams, including data scientists, product managers, and software engineers.

Why Join Us?

• Opportunity to work at the cutting edge of AI and MLOps, leading initiatives that drive real impact.
• Collaborative and dynamic work environment that values innovation and professional growth.
• Up to $200,000 + Share Options + health insurance, stock options, remote work flexibility, etc.