Senior Machine Learning Engineer

- 5-8+ years of professional experience in ML engineering, data engineering, or similar fields. - Expert-level proficiency in Python and ML frameworks (Scikit Learn, TensorFlow, PyTorch, H2O, XGBoost, etc.). - Hands-on experience with AWS SageMaker for training, tuning, deployment, and pipeline automation. - Strong knowledge of H2O.ai (Driverless AI or H2O3), AutoML frameworks, and enterprise ML workflows. - Proficient with MLflow for experiment tracking, model packaging, and deployment. - Advanced experience with PySpark and distributed data processing. - Experience with AWS EMR for Spark cluster management and large-scale data transformations. - Solid understanding of MLOps concepts: CI/CD for ML, feature stores, monitoring, drift detection, model governance. - Strong background in object-oriented programming, algorithm design, and software engineering best practices. - Experience with Docker and containerized ML workloads. Preferred Qualifications: - Knowledge of Kubernetes (EKS) for ML deployment. - Experience implementing model monitoring systems (e.g., Neptune, SageMaker Model Monitor, custom solutions). - Familiarity with microservices, REST APIs, and event-driven architectures. - Experience with large language models (LLMs) and vector databases is a plus. Soft Skills: - Excellent problem-solving and analytical capabilities. - Strong communication and documentation skills. - Ability to function effectively in a fast-paced, cross-functional setting. - Passionate about experimentation, innovation, and continuous improvement.