We are seeking a Machine Learning Engineering Manager to lead and grow a high-performing team of ML engineers. You will be responsible for setting the technical direction, scaling ML infrastructure, ensuring timely delivery of production-ready ML systems, and aligning machine learning initiatives with business goals.
This role blends hands-on technical expertise with people and project leadership. You’ll drive excellence in model development, deployment, and operations, while fostering a collaborative and innovative environment across data science, engineering, and product teams.
Lead, mentor, and manage a team of ML engineers working on production-grade ML solutions.
Define the technical roadmap for ML systems, pipelines, and infrastructure.
Ensure best practices in software engineering, model versioning, testing, deployment, and monitoring.
Oversee project planning, prioritization, and timely delivery of ML initiatives.
Collaborate with cross-functional teams — including data science, product, and platform engineering — to deliver scalable ML solutions.
Implement and maintain MLOps frameworks and scalable infrastructure across the ML lifecycle.
Drive continuous improvement in data quality, model accuracy, and system performance.
Identify opportunities for automation, personalization, and intelligent decision-making through machine learning.
Education:
Bachelor’s or Master’s degree in Computer Science, Machine Learning, Artificial Intelligence, or related field.
MBA or PhD is a plus.
Experience:
10+ years of experience in software or ML engineering, including 3+ years in technical management or team leadership.
Proven track record of delivering production ML systems at scale.
Expertise in machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
Strong proficiency in Python and engineering best practices (unit testing, version control, CI/CD).
Deep knowledge of ML lifecycle, including model training, deployment, serving, and monitoring.
Experience building and managing ML infrastructure using MLOps tools (e.g., MLflow, TFX, Kubeflow, DVC).
Familiarity with cloud platforms (AWS, GCP, Azure) and distributed systems (Spark, Kubernetes).
Strong architectural skills in designing scalable, reliable ML systems.
Excellent leadership, communication, and stakeholder management skills.
Experience hiring, mentoring, and developing high-performing engineering teams.
Exposure to regulated or high-impact domains (e.g., finance, healthcare, retail).
Knowledge of data governance, privacy, and responsible AI principles.
Ability to bridge technical depth with business value in executive-level discussions.