Job Description & Details
This role places you right at the intersection of healthcare technology and modern AI adoption, tasking you with building production-grade LLM and machine learning systems. You won't just be playing with notebooks; you'll be architecting real software that healthcare providers rely on daily. If you like wrestling with complex agent orchestration and cloud-native AI pipelines, this one is worth a serious look.
What You'll Actually Be Doing
Expect to spend your days designing, deploying, and scaling AI-powered features that touch everything from predictive analytics to conversational bots. You'll work closely with product managers and architects to translate vague healthcare challenges into concrete, compliant AI services. Beyond writing code, a big part of your day will involve mentoring the broader engineering team on responsible AI practices, ensuring your models don't just work well, but adhere to strict ethical and security standards.
The Core Tech Stack
You'll need a rock-solid foundation in Python or Java paired with at least four years of hands-on experience building AI and machine learning solutions. They are heavily focused on Generative AI, LLMs, and agentic frameworks like MCP or Strands Agents, so superficial knowledge won't cut it. Because these systems live in production supporting real healthcare workflows, you also need deep familiarity with AWS cloud hosting, robust API development, and the operational rigor required for monitoring and performance tuning.
Interview Expectations
When you sit down with the engineering leads, expect them to probe deeply into how you handle failure states and hallucinations in production LLMs. They'll likely ask you to walk through a time you designed an agent orchestration workflow using frameworks like MCP and how you managed state and safety constraints. The hiring manager is secretly testing whether you understand the messy realities of distributed cloud systems or if you just know how to call an OpenAI wrapper.
Application Advice
Your resume needs to make it immediately obvious that you've shipped AI code to actual cloud environments, not just local prototypes. Make sure keywords like Agentic AI, LLMs, AWS, Python/Java, and conversational AI are front and center to clear the ATS. Tie your past achievements directly to metrics like scalability, latency reduction, or responsible AI compliance to catch the technical recruiter's eye.