Senior Platform Engineer (AI & Cloud)

Who We Are:

Interra Health is a fast-growing healthcare technology company transforming how providers and patients navigate the prescription journey. Formed through the merger of DoseSpot, Arrive Health, and pVerify, Interra Health delivers trusted eligibility, real-time coverage and pricing insights, prescribing tools, and pharmacy transparency at the point of care—helping providers make informed decisions and patients access the right medications with greater clarity and affordability. Backed by strong market momentum and a bold vision for the future of connected care, Interra Health offers the chance to join an innovative, mission-driven team working at the intersection of software and healthcare to reduce friction, improve access, and make the healthcare experience better for everyone.

The Role:

Interra Health is scaling AI across engineering: developer velocity, operational resilience, automation, observability, and secure delivery. The Senior Platform Engineer is the hands-on builder who turns this ambition into working systems. Reporting to the Director, Platform & Operations, this role owns the design, implementation, and operation of AI-enabled platform capabilities, including:

• MCP server configuration and reliability,
• LLMOps/GenAIOps tooling,
• Secure model integration patterns, and
• The underlying Azure cloud infrastructure these capabilities run on.

This is an individual-contributor role, not a people-management seat. You will partner closely with the Lead AI Platform Engineer and the engineers who run the core Azure platform, but you carry direct responsibility for building, hardening, and operating the AI layer itself, from infrastructure as code and CI/CD through to production monitoring, security, and compliance for AI workloads.

The right person has deep DevOps and cloud engineering fundamentals paired with genuine, practical fluency in applying AI to real infrastructure and developer-experience problems.

What You’ll Do:

• Design, build, and operate secure, scalable, highly available Azure infrastructure underpinning both core platform services and AI workloads, using Infrastructure as Code (Terraform).

• Configure, secure, and operate MCP servers and related AI integration points, including access patterns, reliability, and lifecycle management, for enterprise use.

• Build and operationalize AI-enabled capabilities, including internal engineering assistants, workflow automation, infrastructure insights, and incident-response support, applying practical LLMOps/GenAIOps practices for evaluation, monitoring, logging, cost management, and access control.

• Implement and continuously improve CI/CD pipelines in GitHub Actions and DevSecOps practices, integrating security and compliance into AI and infrastructure deployment workflows.

• Own observability and reliability for AI and platform services, applying SRE principles such as SLOs, incident response, and blameless postmortems.

• Partner with the Lead AI Platform Engineer to implement the AI platform roadmap, building reusable patterns, guardrails, and “paved road” solutions for other engineering teams.

• Evaluate, pilot, and support adoption of AI coding assistants and platform tooling across the engineering AI landscape, including GitHub Copilot, M365 Copilot, and other emerging AI dev tools.

• Participate in Agile ceremonies, track and prioritize work in Jira, and influence release planning and architectural decisions.

• Participate in on-call rotations and provide Level 3 support for production platform and AI systems.

• Contribute to internal documentation, runbooks, and knowledge-sharing so AI and platform practices scale beyond any one person.

What You’ll Bring:

• A Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience, plus 6+ years of hands-on experience in DevOps, Cloud Engineering, or Site Reliability Engineering.

• Deep expertise in Microsoft Azure, including infrastructure as code, networking, identity/access, DNS, and load balancing.

• Practical, hands-on experience applying AI, GenAI, or automation tooling to engineering workflows, developer productivity, or production systems, such as LLMOps/GenAIOps, model gateways, RAG patterns, agent frameworks, or AI observability.

• Advanced proficiency with Terraform, containerization and orchestration (Docker, Kubernetes), and CI/CD platforms such as GitHub Actions.

• Proficient in scripting or programming languages (e.g., Python, PowerShell).

• Hands-on comfort with Jira and Agile workflows, and a strong understanding of secure and compliant engineering practices: secrets management, access controls, auditability, and risk-aware implementation.

• Comfortable working autonomously in ambiguous situations, driving clarity through experimentation and iteration in a fast-paced, post-merger environment.

• Excellent communication, able to tailor messaging 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.

• Cloud cost optimization or enterprise platform modernization experience.