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Innovaccer is seeking a Staff Engineer to join its healthcare software platform team. The company builds data integration and AI-powered solutions for hospitals, clinics, and health insurance companies, helping them connect fragmented patient data and automate administrative workflows. Major customers include CommonSpirit Health, Atlantic Health, and Banner Health. The company recently partnered with AWS to scale AI agent capabilities using Amazon Bedrock and AWS HealthLake.
In this role, you will operate across multiple engineering teams and technical domains, owning some of the company's most challenging technical problems. You'll combine hands-on coding with architecture, system thinking, and technical leadership to build scalable, reliable, and intelligent platforms for healthcare.
Key responsibilities include:
- Own and drive large, cross-team technical initiatives from problem definition through architecture, implementation, deployment, and production
- Design and build highly scalable, resilient, secure, and maintainable full-stack and distributed systems
- Solve complex technical problems crossing service, product, platform, and organizational boundaries
- Remain deeply hands-on: designing, coding, debugging, reviewing, and improving production systems
- Use AI coding agents and AI-assisted development extensively across design, implementation, testing, debugging, refactoring, code review, and documentation
- Champion AI-native engineering practices (AIDLC) and help teams evolve development workflows
- Think beyond individual services, understanding system-wide dependencies, trade-offs, failure modes, and customer impact
- Identify opportunities to simplify architecture, eliminate technical debt, and improve engineering velocity and reliability
- Lead architecture and design discussions across teams and influence technical direction
- Partner with Product, AI, Design, and Engineering leaders to translate business problems into technical strategies
- Establish reusable architectural patterns, engineering practices, and technical standards
- Mentor Senior Engineers and help develop the next generation of technical leaders
- Proactively identify important problems before they become incidents or organizational bottlenecks
Requirements:
- 8+ years of software engineering experience with a demonstrated track record of building and operating complex production systems
- Deep hands-on expertise in software engineering, system design, distributed systems, and cloud-native architectures
- Strong coding skills with depth in one or more areas and ability to work effectively across the full stack
- Strong experience with Java, Python, Node.js, TypeScript, React, or equivalent modern technologies
- Strong understanding of databases, APIs, messaging, caching, observability, security, reliability, scalability, and cloud infrastructure
- Demonstrated experience driving architecture and technical initiatives across multiple teams
- Proven ability to take ambiguous, high-impact problems and drive them from problem definition through production
- Strong engineering judgment and ability to balance velocity, scalability, reliability, security, cost, and long-term maintainability
- Strong hands-on proficiency with AI coding tools (Cursor, Claude Code, GitHub Copilot, or equivalent)
- Practical experience with AIDLC / AI-native development, including AI-assisted design, coding, testing, debugging, code review, evaluation, and iterative development
- Strong system-thinking and problem-solving skills across multiple technology stack layers
- Ability to influence technical decisions without relying on organizational authority
- Strong communication skills and ability to work effectively across engineering, product, AI, and business stakeholders
- Demonstrated ownership, curiosity, bias for action, and continuous learning
Good to have:
- Hands-on experience building Agentic AI applications or production AI workflows
- Experience with LLMs, RAG, tool calling, MCP, AI agents, agent orchestration, or multi-agent systems
- Experience designing AI-native or agentic architectures
- Experience with AI evaluation, guardrails, observability, and runtime monitoring
- Experience helping organizations adopt AIDLC and AI-augmented engineering practices
- Experience with AWS, Azure, GCP, Kubernetes, Kafka, Temporal, or similar cloud-native technologies
- Experience defining engineering standards, architectural patterns, or platform-wide technical strategies
