Senior Manager – Mlops

Senior Manager – Mlops

Requirements

• Hands-on experience in building ML pipelines, deploying models to production, and optimizing ML systems.
• Proficiency in programming languages such as Python and familiarity with ML frameworks like TensorFlow, PyTorch, or Scikit-Learn.
• Strong understanding of data engineering, MLOps, API development, and cloud-based ML environments (AWS, Google Cloud, Azure).
• Proven experience leading and mentoring a team of ML or software engineers.
• Track record of delivering complex ML solutions on time with effective team management.
• Excellent communication skills, with the ability to explain ML concepts to non-technical stakeholders.
• Demonstrated experience building cross-functional relationships and fostering consensus.
• Bachelor’s or Master’s degree in Computer Science, Engineering, Machine Learning, or a related field.
• 7+ years of experience in machine learning engineering, software engineering.
• 3+ years of experience in a technical leadership or management role.

Responsibilities

• Lead, mentor, and manage a team of machine learning engineers specializing in model development, deployment, and optimization.
• Foster a collaborative and innovative team environment, upholding our Engineering Operating Principles.
• Drive team performance, professional development, and skills advancement.
• Conduct regular performance reviews, set goals, and provide constructive feedback.
• Provide technical direction on ML system architecture, model deployment pipelines, and scaling solutions.
• Oversee the design and implementation of machine learning solutions that meet requirements for scalability, performance, and reliability.
• Collaborate with cross-functional partners, including product and design, to prioritize and plan the machine learning team’s work.
• Lead the team’s project execution, ensuring alignment with business goals and timely delivery.
• Ensure quality of deliverables, upholding standards for code quality, model performance, and system reliability.
• Own and maintain the ML services and platforms managed by the team.
• Accountable for system availability, setting up monitoring and alerting for key model metrics, and ensuring robust runbooks are in place.
• Act as the subject matter expert on machine learning infrastructure, helping address tactical issues as they arise.
• Work closely with product managers, data scientists, and other stakeholders to understand requirements and deliver ML solutions that meet business needs.
• Continuously seek opportunities to improve ML system performance, reduce model training time, and increase deployment efficiency.
• Stay updated with the latest trends and technologies in machine learning engineering, MLOps, and data science tools.

Preferred Qualifications

• Experience with distributed computing and handling large-scale datasets.
• Knowledge of modern ML engineering and data science tools, including MLflow, Kubeflow, Spark, and containerization tools like Docker and Kubernetes.