MLOps 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).
Hands on experience delivering 3-4 end to end Production projects
Familiarity with data engineering tools (e.g., Airflow, Spark) and ML Ops frameworks.
Solid understanding of software engineering principles and DevOps practices.
Good communication skills and able to manage stakeholders