AI Full Stack Engineer

Your Impact

• Design, build, and scale AI-powered, user-facing products that make Eva’s healthcare interactions more intelligent, intuitive, and reliable.
• Lead the end-to-end delivery of complex full-stack product experiences, from frontend architecture and backend services to APIs, data integrations, and production workflows.
• Partner closely with Product, Design, Data Science, Infrastructure, and other engineering teams to bring AI capabilities into real user workflows in a practical, scalable, and measurable way.
• Translate ambiguous product and AI opportunities into clear technical plans, balancing speed, user experience, reliability, safety, and long-term maintainability.
• Build robust systems that connect application logic, data pipelines, model outputs, and human-centered product experiences.
• Evaluate and integrate AI technologies, including LLM-enabled workflows, retrieval systems, classification, summarization, automation, and decision-support experiences where appropriate.
• Establish strong frontend and backend engineering standards, including component architecture, API design, system reliability, observability, testing, and development best practices.
• Make high-quality technical tradeoffs across product impact, engineering complexity, scalability, and long-term platform health.
• Drive cross-team and cross-org alignment to ship high-impact work, influencing through technical clarity, judgment, and collaboration rather than authority.
• Mentor and grow engineers across the team, raising the bar for technical execution, product thinking, and engineering culture.
• Contribute to both short- and long-term technical roadmaps, especially around AI-enabled product development, platform scalability, and full-stack architecture.
• Help shape how Eva responsibly applies AI in healthcare workflows, with a focus on usefulness, trust, reliability, and excellent user experience.

Your Skillset

• 8+ years of practical full-stack engineering experience.
• A track record of leading the design and delivery of complex consumer or B2B user-facing products across the full stack.
• Deep expertise in modern frontend and backend development, with strong proficiency in technologies such as React, TypeScript, Python, Go, Java, C++, or similar.
• Strong experience designing scalable APIs, backend services, data models, integrations, and distributed systems.
• Hands-on experience with cloud infrastructure and modern DevOps practices, ideally across AWS, GCP, or similar environments.
• Solid understanding of database technologies, including SQL and NoSQL systems, with experience optimizing for performance, reliability, and scale.
• Practical familiarity with AI-enabled product development, such as LLM integrations, model orchestration, prompt/system design, retrieval-augmented generation, evaluation workflows, or AI-assisted automation.
• Strong product judgment and a user-centered approach to building solutions that are robust, scalable, trustworthy, and easy to use.
• Experience working in early-stage startups or similarly fast-moving, rapidly evolving environments; ideally, you have built products from 0 to 1.
• Ability to navigate ambiguity, define technical direction, and deliver high-quality products in fast-paced environments.
• Demonstrated success driving cross-team or cross-org alignment to get impactful work shipped.
• Experience establishing engineering standards, component architectures, reusable patterns, and development best practices.
• Leads through mentorship, actively developing peers and junior engineers while fostering a collaborative, high-performing engineering culture.
• Entrepreneurial mindset: you take initiative, operate with ownership, and drive projects from concept through production impact.

Nice to Have

• Experience building AI-powered products in healthcare, fintech, enterprise SaaS, or other high-trust domains.
• Experience with evaluation, monitoring, and reliability practices for AI systems in production.
• Familiarity with healthcare data, clinical workflows, privacy-sensitive systems, or regulated environments.
• Experience building internal platforms or tooling that accelerate AI product development across engineering teams.