Software Engineer, AI Engineer (Applied​/Software), Backend Developer

We work with a fast‑growing, AI‑native healthcare company that transforms how providers manage administrative operations. By combining modern software infrastructure with applied AI, we build intelligent systems that navigate complex payer workflows, handle repetitive phone‑based processes, and streamline some of the most inefficient parts of the healthcare system.

We are looking for an experienced Staff Software Engineer with strong backend engineering fundamentals and exposure to applied AI systems to join our growing team.

• AI Platform Engineering – Design and build the backend systems that power AI‑driven products, creating scalable services that support language models in production healthcare environments.
• Data Infrastructure – Develop pipelines that ingest, clean, and structure complex healthcare and operational data so models can generate more accurate and context‑aware output.
• Data Model Reliability – Build the tooling needed to monitor performance, measure quality, manage costs, and ensure AI systems behave consistently in production.
• Scalability & Efficiency – Improve how AI workloads run by optimizing throughput, reducing latency, and supporting high‑volume usage across cloud infrastructure.
• Data Security & Governance – Help implement secure systems with strong auditability and data controls suitable for sensitive healthcare data.
• Cross‑Team Partnership – Work closely with product, AI, and clinical teams to turn real‑world healthcare problems into robust technical solutions.
• Technology Ownership – Drive end‑to‑end delivery of LLM backend projects, establish engineering best practices, and mentor peers in LLM system design.

• 5+ years of backend or full‑stack software engineering experience, with 3+ years working on ML/LLM‑enabled applications.
• Strong coding skills in Python and ideally one statically typed language such as Go, Java, or Type Script.
• Experience with LLM integration frameworks (Hugging Face, Lang Chain, Llama Index, OpenAI APIs, Anthropic, etc.).
• Deep knowledge of distributed systems, service‑oriented architecture, and building APIs at scale.
• Familiarity with MLOps/LLMOps practices: CI/CD for models, evaluation harnesses, monitoring, and reproducibility.
• Excellent system design skills and the ability to align technical architecture with product goals.
• Hands‑on experience with RAG pipelines, vector databases, and structured‑output orchestration.
• Background in enterprise SaaS or mission‑critical platforms where uptime, latency, and scale matter.
• Knowledge of responsible AI, safety, and privacy‑preserving ML techniques.