Machine Learning Ops Engineer

You will work as a Machine Learning Ops Engineer at Netsmart, contributing to the design, implementation, and maintenance of ML pipelines for model training, validation, and deployment. Your role involves automating model deployment processes using CI/CD pipelines and containerization technologies. You will be responsible for monitoring model performance, data drift, and system health in production environments, collaborating with data scientists to operationalize machine learning models and algorithms. Additionally, you will implement version control for models, datasets, and ML experiments using MLOps tools, optimize ML infrastructure for scalability, reliability, and cost-effectiveness, and troubleshoot and resolve issues related to model deployment and production systems. It will be part of your responsibility to maintain documentation for ML workflows, deployment processes, and system architecture. This position may require availability outside of standard business hours as part of a rotational on-call schedule. To qualify for this role, you must hold a Bachelor's degree in computer science, Information Management, or a related field. You are expected to have 2-4 years of experience in software development, DevOps, or data engineering. Proficiency in Python, SQL, and at least one ML framework such as TensorFlow, PyTorch, or Scikit-learn is required. Experience with containerization (Docker) and orchestration tools (Kubernetes), cloud platforms like AWS, Azure, GCP, and their ML services, as well as knowledge of CI/CD pipelines, version control (Git), and infrastructure as code are essential. Moreover, familiarity with monitoring tools, logging frameworks for production systems, data pipeline tools like Apache Airflow, Kubeflow, or similar, strong problem-solving skills, and the ability to work in fast-paced, collaborative environments are expected. Preferred skills include experience with MLOps platforms like MLflow, Weights & Biases, Neptune, knowledge of streaming data processing (e.g., Kafka, Kinesis), familiarity with infrastructure monitoring tools such as Prometheus, Grafana, understanding of model interpretability and explainability techniques, experience with feature stores and data versioning tools, and certification in cloud platforms like AWS ML, Azure AI, GCP ML. At Netsmart, you will be part of an inclusive environment that prioritizes your growth, offering professional development opportunities, competitive compensation packages, and comprehensive benefits. Netsmart is an Equal Opportunity Employer.,