Engineering Manager - Machine Learning Platform

Engineering Manager – Machine Learning Platform

Expertise in designing and scaling ML platforms for large-scale AI applications.
Deep experience with ML infrastructure components, such as distributed training, model registries, feature stores, and inference serving.
Hands-on knowledge of ML and data technologies, including TensorFlow, PyTorch, Kubeflow, MLflow, Airflow, Spark, and Kubernetes.
Proficiency in cloud-based ML ecosystems, including AWS (SageMaker, S3, Lambda), GCP (Vertex AI, BigQuery), or Azure ML.
Strong software engineering skills, with experience in Python, Java, or Scala and deep knowledge of ML Ops best practices.
Experience implementing monitoring and observability tools for model drift detection, automated retraining, and performance tracking.
Leadership experience, with a track record of managing engineering teams and collaborating with data scientists.

Design and implement a scalable ML platform, enabling seamless model development, deployment, and monitoring across the organization.
Optimize ML workflows, ensuring efficient experimentation, feature engineering, model training, and inference at scale.
Build and maintain ML infrastructure, including distributed training systems, feature stores, model registries, and real-time serving frameworks.
Work closely with ML engineers and data scientists, providing a self-service platform that accelerates research and deployment cycles.
Ensure compliance and governance, defining best practices for ML model security, monitoring, versioning, and responsible AI practices.
Improve ML model performance, enabling efficient inference pipelines, real-time model serving, and latency optimization.
Lead and mentor a team of ML platform engineers, fostering a culture of innovation and technical excellence.
Stay ahead of ML infrastructure trends, evaluating and adopting emerging technologies to improve scalability and performance.

No preferred qualifications provided.