Senior Platform Engineer - SMTS

Position Summary: We're seeking a Senior Platform Engineer (SMTS) to join in our Analytics & AI division in Bangalore. This role is part of a growing MLOps Engineering team, working on designing, building, and scaling the foundational cloud and Kubernetes infrastructure that powers our ML and analytics workloads. If you're a 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'll be 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 
• 4–9 years of experience in backend or platform engineering 
• Strong programming experience, especially in Python 
• Hands-on expertise in Kubernetes — deploying, managing, and optimizing clusters 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 
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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 and optimize Kubernetes clusters and workloads supporting ML and analytics 
• Enable automated infrastructure provisioning using IaC tools 
• 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