Applied AI Engineer

Hydrogen Group; Palo Alto (CA); On-site; USD 100,000 - 130,000; Full time

Job summary

Hydrogen Group is looking for an experienced AI Engineer in Palo Alto, California, to design, develop, and deploy production-grade AI solutions in healthcare environments. This role requires collaboration with cross-functional teams to build scalable backend systems and AI applications that enhance real-world workflows.

Candidates should have over 4 years of experience in software engineering, with a focus on Python, and hands-on experience delivering AI applications. A solid understanding of backend architecture and API design is crucial.

Qualifications

• 4-6+ years of experience in software engineering, backend engineering, or related field.
• Professional experience building production applications using Python.
• Hands-on experience with AI-powered applications.

Responsibilities

• Design, build, and maintain scalable backend services and APIs for AI applications.
• Develop and deploy generative AI solutions for operational workflows.
• Integrate large language model services into production systems.

Skills

Software engineering

Python

Backend development

RESTful APIs

Problem-solving

Communication

Tools

FastAPI

Terraform

SQL

Job description

Overview

We are seeking an experienced AI Engineer to design, develop, and deploy production-grade AI solutions that address operational challenges within healthcare-related environments. This role involves working closely with cross‑functional teams, stakeholders, and end users to build scalable backend systems and AI‑powered applications that integrate into real‑world workflows. The ideal candidate combines strong software engineering fundamentals with hands‑on experience delivering AI applications in production settings. This is a highly collaborative role focused on building practical, reliable systems rather than experimental research projects.

Key Responsibilities

• Design, build, and maintain scalable backend services and APIs for AI‑driven applications
• Develop and deploy generative AI solutions that support operational and business workflows
• Integrate large language model (LLM) services and third‑party APIs into production systems
• Architect and implement AI agents, automation pipelines, and intelligent workflow solutions
• Collaborate with product, engineering, data, and operational teams to identify business problems and translate them into technical solutions
• Work directly with stakeholders and users to ensure applications align with real‑world processes and requirements
• Build secure, maintainable, and high‑performance systems using modern backend development practices
• Participate in code reviews, sprint planning, testing, and ongoing system optimization
• Own projects end-to-end, from technical design and prototyping through deployment and support

Required Qualifications

• 4‑6+ years of experience in software engineering, backend engineering, solutions engineering, or a related technical role
• Professional experience building and maintaining production applications using Python
• Hands‑on experience developing and deploying AI‑powered applications in live environments
• Strong understanding of backend system architecture and API design principles
• Experience building and consuming RESTful APIs and integrating external services
• Familiarity with relational and/or NoSQL databases
• Ability to work independently while collaborating effectively across technical and non‑technical teams
• Strong problem‑solving, communication, and organizational skills

Preferred Experience

• Experience working with healthcare, laboratory, revenue cycle, supply chain, or operational data systems
• Familiarity with healthcare interoperability and data standards such as HL7 or FHIR
• Exposure to LIS/LIMS platforms, claims processing systems, or healthcare workflow applications
• Experience working in startup or fast‑paced product environments
• Understanding of scalable cloud infrastructure and deployment workflows

Technical Skills & Tools

Core Technologies

• Python
• FastAPI or similar backend frameworks
• SQL
• RESTful APIs
• LLM platforms and AI service integrations

Additional Technologies

• Terraform
• Cloud infrastructure and deployment tooling
• Relational and NoSQL database systems

What Success Looks Like

• Delivering reliable AI‑enabled systems that solve operational challenges at scale
• Translating complex workflows into intuitive technical solutions
• Building maintainable backend services that support high availability and performance
• Taking ownership of critical systems and continuously improving them through iteration and collaboration