Senior Data Scientist

About the Role

The technology that once promised to simplify patient care has brought more issues than anyone ever anticipated. At Innovaccer, we defeat this beast by making full use of all the data Healthcare has worked so hard to collect, and replacing long-standing problems with ideal solutions. Data is our bread and butter for innovation. We are seeking a highly skilled MLOps Engineer to join our dynamic team. The MLOps Engineer will be responsible for deploying, scaling and managing machine learning models in production environments. The ideal candidate will have a strong background in software engineering, data science, and DevOps practices.

A Day in the Life

• ML Deployment Tenets : Design and implement scalable, reliable, and efficient pipelines to deploy machine learning models including LLMs in production. Has understanding of MLOps concepts such as feature stores, model registries, batch and real time inferencing and monitoring as well as feedback design of ML systems. 
• Automation: Design and develop and maintain automation scripts and tools to streamline machine learning workflows, including data preprocessing, model training, and evaluation. 
• Monitoring: Designing monitoring solutions for observability of deployed ML models reliability in production environments.
• Mentoring and Collaboration: Mentor the team to follow ML deployment best practices. Work closely with data scientists, software engineers, and IT teams to integrate machine learning models into existing systems and applications. 
• Continuous Integration/Continuous Deployment (CI/CD): Build and manage CI/CD pipelines to ensure continuous delivery of machine learning models. 
• Optimization: Optimize the performance of machine learning models and infrastructure for scalability and efficiency.
• Participate in the full SDLC process using agile methodology including discovery, inception, story and task creation, breakdown and estimation, iterative planning, development and unit testing, and release/deployment.
• Work with the product teams and to understand their scale and consumption architecture and ensure the MLOps practices to design and build scalable backend solutions for them.

What You Need

• 6+ years experience with at least 4+ years of hands-on experience as an MLOps engineer - expertise in deployment of GenAI solutions in production setup is a plus.
• Python expertise with deployment experience for classical and deep learning models. - Expertise with at least one MLOps frameworks Sagemaker, Databricks or equivalent.
• Demonstrated expertise in deploying deep learning models in production environments at scale. Should be familiar with model hubs and no code platforms to integrate with such as hugging face and AWS bedrock.
• Expertise in DevOps tools and best practices practices, including Docker, Kubernetes, Terraform, and version control systems like Git.
• Demonstrated experience in validating scalable ML/Deep Learning deployments through performance testing frameworks and identifying bottlenecks. Experience handling variations and defining patterns between multiple cloud platforms such as AWS, Azure, or Google Cloud for deploying machine learning models.
• Strong problem-solving skills and the ability to troubleshoot issues in complex systems.
• Basic knowledge of data processing tools and techniques, including SQL and NoSQL databases, data pipelines, and ETL processes.
• Excellent communication and collaboration skills to work effectively with cross-functional teams