Senior Software Engineer - AI Platform

Truveta•Seattle, WA

13h•Hybrid

About The Position

Truveta is seeking a Senior Software Engineer to join their Intelligence Platform and Applications team. The role involves engineering the next wave of healthcare AI by building a reliable, scalable backend platform that powers adaptive AI systems and transforms health data into intelligence. The engineer will help create a foundation that enables clinicians and researchers to make data-driven decisions. The position requires strong platform engineering craftsmanship, production ownership, and cloud-native infrastructure discipline to build scalable services, reliable deployment foundations, and secure AI-enabled workflows. The engineer will shape backend and platform capabilities to make healthcare AI more reliable, maintainable, and impactful, driving progress in research, care delivery, operations, and patient outcomes. This role is based out of Truveta's headquarters in the Greater Seattle area and is a hybrid position.

Requirements

5+ years of software engineering experience, with meaningful ownership of platform, backend, or production infrastructure systems.
Hands-on experience operating cloud-native services in production, including deployment, debugging, reliability, and incident-aware engineering.
Strong experience with Kubernetes, CI/CD pipelines, containers, and infrastructure-as-code tools such as Terraform; AKS and Azure Pipelines experience preferred.
Proficiency in Python for backend or platform service development, including async programming, testing, packaging, and maintainable service design.
Experience with modern Python tooling such as uv, ruff, and ty.
Security-conscious engineering experience, including dependency scanning, CVE triage, token hygiene, OWASP-aligned practices, and secure service design.
Familiarity with LLM APIs such as OpenAI, Azure OpenAI, or equivalent.
B.S. or M.S. in Computer Science, Engineering, Artificial Intelligence, or a related technical field, or equivalent practical experience.

Nice To Haves

Familiarity with agentic AI frameworks or interoperability protocols such as LangGraph, LangChain, MCP, or A2A is a plus.
Experience with artifact registry management, including JFrog, Docker/PyPI proxying, GPU artifact mirroring, or similar workflows is a plus.

Responsibilities

Build and operate cloud-native platforms using Docker, devcontainers, Kubernetes, CI/CD pipelines, and infrastructure-as-code patterns, working across AKS, Terraform/Terragrunt, and deployment pipelines.
Build and own production services end to end, including designing, building, deploying, debugging, and operating backend services in production using async-first, well-tested Python and modern tooling.
Think and build like platform engineers, focusing on modular architecture, separation of concerns, code quality, and pragmatic design trade-offs to translate product and AI platform needs into robust backend systems.
Improve deployment and operational reliability by contributing to CI/CD template maintenance, deployment stage configuration, pipeline hardening, and production environment debugging.
Bring a security and reliability mindset, experienced with dependency health, Snyk/CVE triage, library upgrades, token hygiene, OWASP practices, and security patching within SLA.
Manage artifacts and dependencies responsibly, experienced with artifact registry workflows such as JFrog, Docker/PyPI proxying, GPU artifact mirroring, or similar dependency management practices.
Have practical exposure to AI-enabled systems, familiar with LLM APIs such as OpenAI, Azure OpenAI, or equivalent, and able to contribute meaningfully to AI-powered workflows.
Understand agentic AI patterns, interested in or familiar with LangGraph, LangChain, MCP, A2A, or similar agent orchestration, tool integration, or interoperability patterns.
Collaborate across boundaries, partnering effectively with platform, ML, application, and product engineers to transform concepts into scalable, reliable solutions.
Demonstrate senior-level ownership, capable of taking services or features from design to production, mentoring peers, and making pragmatic trade-offs.
Act with purpose, applying thoughtful engineering and security-first principles to create systems that advance healthcare intelligence responsibly and at scale.