Senior ML Ops Engineer

3 days ago

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Datavant

WebsiteLinkedInAll Job Openings

201 - 500 employees

Founded 2017

⚕️ Healthcare Insurance

☁️ SaaS

🏢 Enterprise

💰 $40M Series B on 2020-10

Description

• Datavant is a data platform company for healthcare, enabling data movement and connection securely. • We seek a skilled MLOps Engineer with expertise in Spark, Python, GPU and preferably Databricks. • Collaborate closely with data scientists, software engineers, and DevOps teams to deploy and manage ML models. • Daily responsibilities include designing, implementing, and maintaining scalable MLOps infrastructure and pipelines. • Develop and automate CI/CD pipelines for model training, testing, validation, and deployment. • Implement monitoring, logging, and alerting solutions to track model performance and system health. • Optimize machine learning workflows for performance, scalability, and cost efficiency. • Ensure security and compliance throughout the MLOps lifecycle. • Provide technical guidance and support to cross-functional teams on MLOps best practices. • Stay updated on emerging technologies, tools, and practices in MLOps.

Requirements

• Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics, or a related field. • Proven experience (5+ years) as a MLOps Engineer, Software engineer, DevOps Engineer or related role. • Excellent communication and collaboration skills, with the ability to work effectively in cross-functional teams. • Strong understanding of machine learning concepts, algorithms, and frameworks such as MLFlow, TensorFlow, PyTorch, or Scikit-learn. • Knowledge of big data processing technologies such as Apache Spark for handling large-scale data and distributed computing. • Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform (GCP) and familiarity with services like AWS SageMaker, Azure Machine Learning, or Google AI Platform. • Understanding of containerization technologies like Docker and container orchestration tools like Kubernetes for managing machine learning workflows in production environments. • Proficiency in version control systems (e.g., Git) and CI/CD tools for automating the deployment and management of machine learning models. • Hands-on experience with Databricks for data engineering and analytics (nice to have). • Experience designing and implementing CI/CD pipelines for machine learning workflows using tools like Jenkins, GitLab CI, or Azure DevOps. • Knowledge of version control systems (e.g., Git) and collaborative development workflows. • Strong problem-solving skills and attention to detail, with the ability to troubleshoot complex issues in distributed systems.