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

Requirements:
•           12+ Years of professional experience in Software Engineering & 5+ 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.