Lead Backend Engineer - AI Services

Job Description

Lead Backend Engineer – AI Services

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Location: 100% Remote — U.S.

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Employment: Full Time

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Compensation: $175,000–$205,000 base

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

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About the Company

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

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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.

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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.

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About the Role

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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.

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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.

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

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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.

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What You’ll Do

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

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

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• Python and FastAPI
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• MLflow
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• LangChain or similar orchestration frameworks
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• Vector databases
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• Retrieval-Augmented Generation (RAG)
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• LLM APIs
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• Model serving
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• ML pipelines
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• Feature stores
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• Prompt engineering
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• Agentic workflows
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This is not an AI research position. The priority is understanding how these technologies can be integrated into reliable, scalable, customer-facing production software.

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What Will Set You Apart

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

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

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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.

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Why Join

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