Job
Description
Position Summary: We'reseeking a Senior Platform Engineer (SMTS) to join in our Analytics & AI division in Bangalore. This role is part of a growingMLOpsEngineering team, working on designing, building, and scaling the foundational cloud and Kubernetes infrastructure that powers our ML and analytics workloads. Ifyou'rea strong backend/platform engineer with a passion for Python, Kubernetes, and public cloud platforms (preferably AWS), this is an exciting opportunity to drive impact at scale. ? About the Team: The Analytics & AI team harnesses petabytes of healthcare data to power intelligent, actionable insights. Our Platform Engineering team focuses on creating the underlying infrastructure and services that enable robust data science, machine learning, and analytics across athenahealth's ecosystem.You'llbe a core contributor in enabling scalable, secure, and efficient ML operations. Ideal Qualifications: Bachelor's degree in computer science, Software Engineering, or a related field 49years of experience in backend or platform engineering Strong programming experience, especially in Python Hands-onexpertisein Kubernetes deploying, managing, andoptimizingclusters and workloads Experience with cloud infrastructure (preferably AWS) and tools like Terraform or CloudFormation Familiarity with Kubeflow or other ML orchestration tools (a plus, not mandatory) Strong knowledge of CI/CD, containerization, and cloud-native architectures Experience in working with observability tools like Grafana, Prometheus, and CloudWatch Understanding of cloud security, service mesh (e.g., Istio), and infrastructure as code Experience with databases like Snowflake, PostgreSQL, MySQL, or Redis is a plus Job Responsibilities: Platform Engineering & Infrastructure (50%) Design, implement, and scale cloud-native infrastructure and internal platform tools Build backend services and automation frameworks in Python Manage andoptimizeKubernetes clusters and workloads supporting ML and analytics Enable automated infrastructure provisioning usingIaCtools Ensure systems are secure, reliable, and scalable for production use Collaboration & Delivery (30%) Partner with data science, DevOps, and engineering teams to support their deployment pipelines Lead or contribute to architecture/design discussions and technical decisions Contribute to agile practices including sprint planning, retrospectives, and backlog grooming Reliability & Optimization (20%) Own performance, reliability, and observability of deployed platforms Improve logging, alerting, and monitoring systems Promote platform best practices across teams and mentor junior engineers
