ML Ops Engineer

Key Responsibilities

• Develop and maintain ML pipelines using tools like MLflow, Kubeflow, or Vertex AI.
• Automate model training, testing, deployment, and monitoring in cloud environments (e.g., GCP, AWS, Azure).
• Implement CI/CD workflows for model lifecycle management, including versioning, monitoring, and retraining.
• Monitor model performance using observability tools and ensure compliance with model governance frameworks (MRM, documentation, explainability)
• Collaborate with engineering teams to provision containerized environments and support model scoring via low-latency APIs
• Leverage AutoML tools (e.g., Vertex AI AutoML, H2O Driverless AI) for low-code/no-code model development, documentation automation, and rapid deployment

Qualifications

• 10+ Years of professional experience in Software Engineering & 3+ Years in AIML, Machine Learning Model Operations.
• Strong proficiency in Java and Python, SQL, and ML libraries (e.g., scikit-learn, XGBoost, TensorFlow, PyTorch).
• Experience with cloud platforms and containerization (Docker, Kubernetes).
• Familiarity with data engineering tools (e.g., Airflow, Spark) and ML Ops frameworks.
• Solid understanding of software engineering principles and DevOps practices.
• Ability to communicate complex technical concepts to non-technical stakeholders.