Job Description
Lead Backend Engineer – AI
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Location: Fully remote within the United States
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Employment Type: Full-time, permanent
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Role Overview
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We are partnering with a healthcare analytics SaaS company to hire a Lead Backend Engineer – AI. This hands-on technical leader will evolve core backend services while building production-ready AI and machine learning capabilities.
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You will work across backend engineering, cloud infrastructure, data science, and applied AI to productionize LLM workflows, RAG systems, prediction and scoring services, model serving, and agentic applications. This role is ideal for an engineer with deep Java expertise and practical Python/ML experience who can make sound architectural and build-versus-buy decisions.
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Responsibilities
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- Lead the design and development of scalable backend services for the healthcare analytics platform
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- Build AI-enabled capabilities including LLM integrations, RAG workflows, predictive and scoring services, model serving, and agentic applications
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- Partner with Data Science to move models and proof-of-concepts into reliable production systems
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- Develop APIs and services using server-side Java and Python/FastAPI
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- Build and maintain ML pipelines, model deployment workflows, and model lifecycle processes
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- Use tools such as MLflow for experiment tracking, model management, and deployment
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- Design retrieval systems using vector databases, embeddings, and document search
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- Evaluate build-versus-buy decisions across AI tooling, model providers, orchestration platforms, and vector stores
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Requirements
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- 10+ years of software engineering experience, including substantial backend or distributed systems work
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- Strong server-side Java development experience
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- Production experience with Linux, cloud infrastructure, and scalable service architectures
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- Hands-on experience building and operating AI or machine learning systems in production
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- Strong Python experience, including FastAPI
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- Experience with LLM applications, LangChain or similar orchestration frameworks, and agentic workflows
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- Experience with vector databases, embeddings, RAG architectures, and retrieval systems
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- Experience with model serving, ML pipelines, model deployment, and lifecycle management
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- Familiarity with MLflow or comparable tools for experiment tracking and model registry
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Nice to Haves
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- Healthcare, healthcare analytics, health tech, or regulated SaaS experience
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- Experience with sensitive data, security, privacy, or compliance-focused systems