Description

Design, build, deploy, and maintain enterprise-scale ML models and end-to-end pipelines.

This role is on-site.

Responsibilities

• Architect and implement scalable ML solutions, including data preparation, feature engineering, and model optimization.
• Deploy and monitor models in production, managing model drift detection, retraining, and MLOps automation.
• Drive governance, reliability, and CI/CD practices for machine learning systems.
• Mentor engineers and provide technical leadership on emerging AI/ML technologies.
• Collaborate with data engineering and DevOps teams to deliver robust ML infrastructure.

Required Skills

• Strong Python expertise with NumPy, Pandas, and Scikit-learn.
• Hands-on experience with TensorFlow and/or PyTorch.
• Experience building and deploying ML solutions on AWS, Azure, or GCP.
• Knowledge of ML pipelines, model evaluation, version control, and MLOps practices.
• Strong understanding of ML system design and performance optimization.
• Bachelor's degree and 10+ years of experience.

Preferred Skills

• Experience with MLflow, SageMaker, Azure ML, or similar MLOps platforms.
• Knowledge of Docker, Kubernetes, Spark, or Ray.

Key Skills

Education

Bachelor's degree