About the Company At Netsmart you’ll work on exciting challenges that shape the future of (industry/domain). You’ll have the opportunity to collaborate with talented professionals passionate about technology. Work in a supportive and inclusive environment where your growth is prioritized. Access professional development opportunities, including certifications and training. Enjoy a competitive compensation package and comprehensive benefits. About the Role Machine Learning Ops Engineer Responsibilities Design, implement and maintain ML pipelines for model training, validation, and deployment Automate model deployment processes using CI/CD pipelines and containerization technologies Monitor model performance, data drift, and system health in production environments Collaborate with data scientists to operationalize machine learning models and algorithms Implement version control for models, datasets, and ML experiments using MLOps tools Optimize ML infrastructure for scalability, reliability, and cost-effectiveness Troubleshoot and resolve issues related to model deployment and production systems 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 Qualifications Bachelor's degree in computer science, Information Management or related field Required Skills 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, Scikit-learn Experience with containerization (Docker) and orchestration tools (Kubernetes) Knowledge of cloud platforms such as AWS, Azure, GCP and their ML services Understanding of CI/CD pipelines, version control (Git), and infrastructure as code Familiarity with monitoring tools and logging frameworks for production systems Experience with data pipeline tools such as Apache Airflow, Kubeflow, or similar Strong problem-solving skills and ability to work in fast-paced, collaborative environments Preferred Skills Experience with MLOps platforms such as MLflow, Weights & Biases, Neptune Knowledge of streaming data processing such as 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 Certification in cloud platforms such as AWS ML, Azure AI, GCP ML Competitive compensation package and comprehensive benefits. We’re An Equal Opportunity Employer.
Job Title
Machine Learning Ops Engineer