Description

You will design, develop, and deploy AI/ML models for predictive analytics, automation, and decision support within enterprise systems.

This role is on-site.

Responsibilities

• Build and maintain data pipelines for model training, tuning, and deployment using ETL/ELT and distributed processing.
• Develop and optimize ML algorithms with Python, TensorFlow, PyTorch, or Scikit-learn to ensure accuracy and performance.
• Deploy models into production via cloud platforms (AWS SageMaker, Azure ML, OCI) and integrate with enterprise APIs.
• Implement MLOps practices, including CI/CD pipelines, automated deployment, and continuous monitoring for drift and retraining.
• Ensure solutions meet security, compliance, and enterprise architecture standards while collaborating with DevSecOps and data engineering teams.

Required Skills

• 5+ years of experience with Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn).
• Hands-on experience with cloud AI/ML services: AWS SageMaker, Azure ML, or OCI Data Science.
• Strong understanding of data engineering concepts, including ETL/ELT, feature stores, and distributed processing.
• Proficiency in MLOps practices, CI/CD pipelines, and automated model deployment workflows.
• Bachelor's degree in a relevant field.
• Knowledge of security and compliance requirements for government or regulated environments.
• Ability to translate complex analytical concepts into clear, actionable insights for technical and non-technical audiences.

Key Skills

Education

Bachelor's degree