Note: The job is a remote job and is open to candidates in USA. Harnham is recruiting for a healthcare technology company building SaaS products that use complex data to support faster, more informed decisions. The Lead Backend Engineer – AI Services will lead hands-on backend engineering and integrate AI and machine learning capabilities into reliable, scalable, customer-facing production services. The role partners with Data Science to productionize models, build reusable AI infrastructure, and drive initiatives from architecture through deployment and operations.
Responsibilities
- 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
Skills
- Work Authorization: Must be legally authorized to work in the U.S. without employer sponsorship
- • 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
- • 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
- • 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
Benefits
- 100% Remote — U.S.
- 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
Company Overview