About the role:

We are seeking a skilled MLOps Engineer with expertise in Spark, Python, GPU and preferably Databricks to join our team. As an MLOps Engineer, you will play a critical role in operationalizing and automating machine learning workflows, ensuring scalability, reliability, and efficiency. You will collaborate closely with data scientists, software engineers, and DevOps teams to deploy, monitor, and manage machine learning models in production environments.

Who you are: 

Daily responsibilities include Development and Management of key system areas including:

• Design, implement, and maintain scalable MLOps infrastructure and pipelines using Apache Spark, Python, and other relevant technologies.
• Collaborate with data scientists and software engineers to deploy machine learning models into production environments.
• Develop and automate CI/CD pipelines for model training, testing, validation, and deployment.
• Implement monitoring, logging, and alerting solutions to track model performance, data drift, and system health.
• Optimize and tune machine learning workflows for performance, scalability, and cost efficiency.
• Ensure security and compliance requirements are met throughout the MLOps lifecycle.
• Work closely with DevOps teams to integrate machine learning systems with existing infrastructure and deployment processes.
• Provide technical guidance and support to cross-functional teams on best practices for MLOps and model deployment.
• Stay updated on emerging technologies, tools, and best practices in MLOps and machine learning engineering domains.
• Perform troubleshooting and resolution of issues related to machine learning pipelines, infrastructure, and deployments.

What you bring to the table:

• 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.

Nice to Have

• Masters degree in information technology, computer science, software engineering, or data science preferred
• Healthcare Domain expertise
• Experience productionizing large NLP models