Senior Platform Engineer

Role overview This is a hands-on senior platform engineering role focused on building and operating AI-enabled platform capabilities for a healthcare technology organization scaling AI across engineering workflows. The position owns secure Azure infrastructure, MCP server configuration, LLMOps/GenAIOps tooling, and the integration patterns that let AI workloads run reliably in production. It is an individual-contributor seat with direct impact on developer velocity, operational resilience, and the AI layer that other engineering teams adopt.

Responsibilities - Design, build, and operate secure, highly available Azure infrastructure using Terraform, including networking, identity, DNS, and load balancing for core and AI workloads. - Configure, secure, and operate MCP servers and AI integration points, covering access patterns, reliability, and lifecycle management. - Build AI-enabled capabilities such as internal engineering assistants, workflow automation, infrastructure insights, and incident-response support, with practical LLMOps/GenAIOps practices for evaluation, monitoring, logging, cost management, and access control. - Implement and improve CI/CD pipelines in GitHub Actions, integrating DevSecOps and compliance into AI and infrastructure deployment workflows. - Own observability and reliability using SRE principles (SLOs, incident response, blameless postmortems), and participate in on-call rotations with Level 3 production support. - Partner with the Lead AI Platform Engineer to deliver the AI platform roadmap and reusable "paved road" patterns, and evaluate AI coding assistants with a build-versus-buy mindset.

Requirements - Bachelor's degree in Computer Science or related field, or equivalent practical experience, plus 6+ years in DevOps, Cloud Engineering, or SRE. - Deep expertise in Microsoft Azure infrastructure as code, networking, identity/access, DNS, and load balancing. - Practical, hands-on experience applying AI, GenAI, or automation tooling to engineering workflows, such as LLMOps/GenAIOps, model gateways, RAG patterns, agent frameworks, or AI observability. - Advanced proficiency with Terraform, Docker, Kubernetes, CI/CD platforms like GitHub Actions, and scripting/programming languages such as Python or PowerShell. - Comfort with Jira and Agile workflows, plus strong understanding of secure and compliant engineering practices. - Ability to work autonomously in ambiguous, fast-paced situations, with excellent communication across engineering and business audiences.

Nice to have - Experience in healthcare technology, regulated SaaS, or HIPAA/HITRUST/SOC 2 environments. - Direct MCP server implementation or administration experience, and cloud cost optimization or platform modernization background.

Benefits and work setup - Individual-contributor position with no people management responsibility, partnering closely with the Lead AI Platform Engineer and core Azure platform team.