ML Ops Engineer

Job Description Note: Onsite Role In-person Interview Must 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. 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 Required Skills: Software engineering, AIML, Machine Learning Model Operations, Java, Python, SQL, Scikit-learn, XGBoost, TensorFlow, PyTorch, Docker, Kubernetes, Airflow, Spark