Description

You will own the end-to-end MLOps lifecycle, bridging the gap between experimental ML research and production-grade software engineering. Your work ensures that machine learning models are deployed reliably, monitored continuously, and governed securely.

Responsibilities

• Design and maintain CI/CD pipelines for machine learning models and data infrastructure.
• Automate model training, validation, testing, deployment, and rollback processes.
• Deploy ML models as REST APIs or microservices within production environments.
• Implement model monitoring, data drift detection, and performance tracking systems.
• Manage and optimize cloud infrastructure for scalability, cost efficiency, and security.

Required Skills

• 5–8 years of experience in Software Engineering, Data Engineering, or ML Engineering.
• 3+ years of hands-on experience in MLOps or ML Platform Engineering.
• Strong Python programming skills for ML development and automation.
• Proficiency with TensorFlow, PyTorch, or Scikit-learn.
• Experience with Docker containerization and Kubernetes orchestration.
• Hands-on experience with CI/CD tools: GitHub Actions, Jenkins, or Argo CD.
• Proven track record deploying and maintaining production ML systems.
• Understanding of MLflow, DVC, Kubeflow, or similar experiment and data versioning tools.

Preferred Skills

• Experience with feature stores and pipeline orchestration tools.
• Familiarity with AWS, Azure, or GCP cloud platforms.

Key Skills

Education

Bachelor's degree