Senior Software Engineer - AI Platform

Truveta•Seattle, WA

1d•Hybrid

About The Position

Join Truveta’s Intelligence Platform and Applications team to engineer the next wave of healthcare AI, building the reliable, scalable backend platform that powers adaptive AI systems and helps transform health data into intelligence. Guided by Truveta’s mission of Saving Lives with Data, you’ll help create an engineering foundation that enables clinicians and researchers to make data-driven, proactive, and impactful decisions. We are seeking a Senior Engineer who thrives at the intersection of platform engineering, cloud-native infrastructure, backend services, and applied AI systems. You’ll bring strong platform engineering craftsmanship, production ownership, and cloud-native infrastructure discipline to build scalable services, reliable deployment foundations, and secure AI-enabled workflows that support healthcare intelligence at scale. You’ll help shape the backend and platform capabilities that make healthcare AI more reliable, maintainable, and impactful, driving real-world progress across research, care delivery, operations, and patient outcomes. Patients, doctors, and medical researchers deserve to benefit from the same large-scale technological innovations and artificial intelligence that have transformed productivity, creativity, and communication. As part of the Intelligence Platform and Applications team, you’ll use your expertise in platform engineering, cloud-native infrastructure, backend services, and production systems to build the robust foundation that intelligent healthcare applications depend on. If you’re seeking a sense of purpose and are motivated to advance how technology supports learning, decision-making, and discovery in healthcare, you’ll find this an inspiring place to grow. You’ll work in an ambitious, fast-paced, collaborative environment where every contribution helps make healthcare more connected, intelligent, and impactful.

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: hands-on with Docker, devcontainers, Kubernetes, CI/CD pipelines, and infrastructure-as-code patterns; comfortable working across AKS, Terraform/Terragrunt, and deployment pipelines.
Build and own production services end to end: experienced in designing, building, deploying, debugging, and operating backend services in production, using async-first, well-tested Python and modern tooling such as uv , ruff , and ty to deliver reliable, maintainable systems.
Think and build like platform engineers: grounded in modular architecture, separation of concerns, code quality, and pragmatic design trade-offs. You can translate product and AI platform needs into robust backend systems that integrate cleanly into a large-scale platform.
Improve deployment and operational reliability: able to contribute 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 without needing to be an ML researcher.
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: partner effectively with platform, ML, application, and product engineers to transform concepts into scalable, reliable solutions. You communicate clearly, share knowledge openly, and thrive in cross-functional teams.
Demonstrate senior-level ownership: capable of taking services or features from design to production, mentoring peers, and making pragmatic trade-offs that balance speed, quality, security, and reliability.
Act with purpose: applying thoughtful engineering and security-first principles to create systems that advance healthcare intelligence responsibly and at scale.