MLOps Engineer

Key Responsibilities

• Design, develop, and maintain end-to-end ML pipelines using tools such as MLflow, Kubeflow, Vertex AI.
• Automate model training, validation, deployment, monitoring, and retraining in cloud environments (Google Cloud Platform, Azure, AWS).
• Implement and manage CI/CD workflows for the ML lifecycle, including:
  • Model versioning
  • Performance monitoring
  • Automated retraining
• Monitor model health and performance using observability tools; ensure compliance with model governance frameworks (MRM, documentation, explainability).
• Collaborate with engineering teams to:
  • Provision containerized environments (Docker, Kubernetes)
  • Support low-latency model scoring APIs
• Leverage AutoML platforms such as:
  • Vertex AI AutoML
  • H2O Driverless AI for rapid model development, deployment, and documentation automation.
• Work closely with cross-functional stakeholders to translate business problems into scalable ML solutions.

• 10+ years of professional experience in Software Engineering.
• 3+ years of hands‑on experience in AI/ML and MLOps.
• Strong experience in both ML Engineering and Data Science concepts.
• Proficiency in:
  • Python & Java
  • SQL
  • ML libraries: scikit‑learn, XGBoost, Tensor Flow, Py Torch
• Strong hands‑on experience with Spark (PySpark preferred) for large-scale data processing.
• Experience with cloud platforms, with strong preference for:
  • Google Cloud Platform (Vertex AI, AutoML)
  • Microsoft Azure
• Expertise in:
  • Docker
  • Kubernetes
  • ML pipeline orchestration
• Experience with data engineering and workflow tools such as:
  • Apache Airflow
  • Spark
• Solid understanding of Dev Ops, CI/CD, and software engineering best practices.

Mid‑Senior level

Full‑time

Engineering and Information Technology

Software Development