Lead Backend Engineer - AI Services

Lead Backend Engineer – AI Services

Location: 100% Remote — U.S.

Employment: Full Time

Compensation: $175,000–$205,000 base

Work Authorization: Must be legally authorized to work in the U.S. without employer sponsorship

About the Company

A growing healthcare technology company is building SaaS products that help organizations use complex data to make faster, more informed decisions.

The company is investing heavily in AI across its technology platform and is expanding the engineering capabilities required to bring AI and machine learning into production.

This is an opportunity to join a highly technical environment where backend engineering, healthcare data, analytics, and AI come together to solve complex real-world problems.

About the Role

The Lead Backend Engineer – AI Services will be a hands-on technical leader responsible for evolving an established backend platform and integrating AI and machine learning capabilities into customer-facing products. The focus is taking AI from prototype to production.

You’ll work closely with Data Science to transform proof-of-concept models and workflows into scalable, reliable production services while establishing reusable infrastructure and engineering patterns that accelerate future AI development. This is not a management role, pure architecture position, or greenfield AI research opportunity.

You’ll remain deeply hands-on, build within an established production platform, make architectural decisions, and personally drive complex initiatives from design through deployment.

Healthcare experience is required. The strongest candidates will combine deep server-side Java and backend engineering expertise with experience building technology in healthcare environments and successfully integrating AI/ML into commercial products.

What You’ll Do

• Build and evolve backend services within an established SaaS platform
• Integrate AI capabilities including LLMs, model serving, RAG, predictions, and scoring
• Partner closely with Data Science to move POCs, models, and AI workflows into production
• Design reusable services, APIs, integrations, and infrastructure for future AI capabilities
• Build orchestration and retrieval services supporting production AI applications
• Lead complex initiatives from architecture through implementation and production
• Enable rapid AI experimentation while maintaining scalability, reliability, security, and maintainability
• Create repeatable patterns for transitioning successful experiments into customer-facing capabilities
• Influence backend platform architecture and engineering standards
• Evaluate emerging AI technologies, frameworks, and deployment approaches
• Make build-vs.-buy decisions and communicate technical tradeoffs to leadership
• Own solutions end-to-end across testing, deployment, debugging, monitoring, and production operations

Must Haves

• 15+ years of Software Engineering experience
• Healthcare technology experience
• Experience building software, platforms, or data-intensive applications within healthcare environments
• Deep server-side Java experience
• Strong backend and platform engineering background
• Experience building and modernizing existing production SaaS platforms
• Proven experience integrating AI/ML into commercial or customer-facing products
• Experience taking AI or ML initiatives from prototype through production
• Strong architecture and system design capabilities
• Experience designing scalable APIs, services, data flows, and integration patterns
• Experience working closely with Data Science or ML teams
• Strong Software Engineering fundamentals across testing, CI/CD, monitoring, security, debugging, and source control
• Production Linux experience
• Cloud experience across AWS, Azure, or GCP
• SQL knowledge and experience working with data-centric platforms
• Ability to independently drive technically complex projects from beginning to end
• Strong communication skills and the ability to explain architectural decisions and technical tradeoffs to leadership

Candidates should have working knowledge across several areas of the modern AI application stack, including:

• Python and FastAPI
• MLflow
• LangChain or similar orchestration frameworks
• Vector databases
• Retrieval-Augmented Generation (RAG)
• LLM APIs
• Model serving
• ML pipelines
• Feature stores
• Prompt engineering
• Agentic workflows

This is not an AI research position. The priority is understanding how these technologies can be integrated into reliable, scalable, customer-facing production software.

What Will Set You Apart

• You've personally taken AI/ML capabilities from POC through production
• You have deep Java backend engineering experience rather than an AI-only background
• You've built or modernized production technology within healthcare
• You understand the complexities of healthcare data and healthcare-focused applications
• You've built reusable AI platform capabilities rather than isolated prototypes
• You're comfortable working across Java-based backend systems and Python-based AI ecosystems
• You've worked closely with Data Scientists and understand how to productionize their work
• You have experience with healthcare analytics, decision-support, or other data-intensive healthcare applications
• You can independently make architecture and build-vs.-buy decisions
• You remain highly hands-on despite operating at Lead/Principal-level scope

Team & Environment

You’ll join a small, senior Platform Engineering team and work closely with Data Science and engineering leadership.

This is a technical leadership position rather than a people-management role. The expectation is leadership through architecture, engineering judgment, technical direction, and hands-on execution.

Why Join

• Own the engineering work that takes AI from experimentation into customer-facing production
• Help establish the backend architecture required to scale AI across an established SaaS platform
• Build reusable AI services rather than one-off proofs of concept
• Work directly with Data Science to productionize models and AI capabilities
• Influence architecture, technical standards, and build-vs.-buy decisions
• Work across LLMs, RAG, model serving, orchestration, and emerging agentic capabilities
• Remain deeply hands-on while operating with Lead-level technical ownership
• Solve complex problems at the intersection of backend engineering, healthcare data, analytics, and AI