athenahealth - Lead MLOps Engineer - Python/Cloud Computing

Ideal Qualifications : - Bachelors degree in Computer Science, Software Engineering, or a related discipline. - 7 to 12 years of experience in software engineering, with expertise in MLOps, cloud computing, and scalable architectures. - Strong object-oriented programming skills, preferably in Python. - Hands-on experience in developing and deploying microservices in any public cloud environment such as AWS, Azure, or GCP. - Expertise in Kubernetes, including designing, deploying, and maintaining enterprise-class ML models and services. - Experience in Kubeflow, maintaining and optimizing ML pipelines for efficient model training and deployment. - Proven experience in deploying and maintaining Linux-based, highly scalable, and fault-tolerant enterprise platforms. - Hands-on experience with Terraform or CloudFormation for infrastructure automation and cloud resource management. - Familiarity with monitoring and logging tools such as Grafana, Prometheus, and CloudWatch. - Strong understanding of cloud security, service mesh architectures (Istio), and scalable ML deployment best practices. - Experience working with databases such as Snowflake, PostgreSQL, MySQL, Redis, and DynamoDB. - Proficiency in configuration management and CI/CD tools like Jenkins, Puppet, Chef, and Bottlerocket. Job Responsibilities : Technical Execution (50%) - Produce clear and detailed technical design specifications for ML and cloud-based solutions. - Develop, test, and deploy high-quality software components that align with security, performance, and scalability requirements. - Design and maintain Kubernetes-based ML model deployments in cloud environments. - Optimize and manage Kubeflow pipelines for model training, deployment, and monitoring. - Implement cloud infrastructure automation using Terraform or CloudFormation. - Ensure best practices in cloud security, monitoring, and scalability. - Conduct unit testing, functional testing, and peer code reviews to maintain code quality and reliability. Contribution to the team (30%) - Take ownership of deployed models and ensure their continuous improvement. - Participate actively in agile ceremonies such as stand-ups, sprint planning, retrospectives, and backlog grooming. - Work collaboratively with data scientists, ML engineers, and software developers to integrate ML models into production Coordination and Communication (10%) - Collaborate with technology and product teams to align ML initiatives with business goals. - Share technical knowledge and insights across teams to enhance collective expertise. Mentorship and Leadership (10%) - Mentor and support junior engineers to improve overall team productivity. - Promote best practices and innovation within the team to drive MLOps success (ref:hirist.tech)

Location: bangalore, IN

Posted Date: 4/19/2025