AI Engineer

We are seeking a highly skilled and hands-on **AI Solutions Engineer / Forward Deployed Engineer (FDE)**with strong US Healthcare domain expertise and experience building Generative AI / RAG solutions on Azure. The ideal candidate combines a strong software engineering foundation in Python with practical experience developing RAG applications using LangChain, LangGraph, LlamaIndex or similar frameworks.

The engineer will work directly with business and healthcare stakeholders to understand business workflows, identify opportunities for AI-driven transformation, solutionize complex problems, and build production-ready solutions. The customer has an established .NET application ecosystem, so experience in C#/.NET is preferred for integrating and modernizing existing applications with new GenAI capabilities.

Key Responsibilities

• Partner directly with US Healthcare business stakeholders to understand workflows, pain points, requirements and business objectives.
• Translate healthcare business problems into scalable AI/GenAI technology solutions.
• Design and develop production-grade RAG applications using enterprise and healthcare data.
• Build GenAI solutions using Lang Chain, Lang Graph, Llama Index or comparable frameworks.
• Develop backend services and AI integrations using Python and C#/.NET.
• Integrate GenAI/RAG capabilities with existing .NET-based enterprise applications.
• Design retrieval pipelines, document ingestion, chunking, embeddings, metadata strategies and context-retrieval workflows.
• Develop secure and scalable AI applications using Azure cloud services.
• Build REST APIs, microservices and application integrations required to operationalize GenAI solutions.
• Work with frontend teams to develop or integrate user-facing experiences using React; candidates with Angular experience should be comfortable transitioning to React.
• Collaborate with architecture, engineering, product and business teams throughout the solution lifecycle.
• Participate in solution discovery, technical design, development, testing, deployment and production support.
• Identify opportunities to modernize existing healthcare applications using GenAI and intelligent automation.
• Ensure solutions meet enterprise requirements around security, scalability, performance, reliability and healthcare data protection.
• Contribute to engineering standards, code reviews, technical documentation and knowledge sharing.

Required Technical Skills

Software Engineering

• Strong hands-on experience with C# and .NET / ASP.NET Core
• Strong hands-on Python development experience
• REST APIs and microservices
• SQL / NoSQL databases
• Enterprise application integration
• Object-oriented design and software engineering best practices

Domain Expertise:

• US Healthcare domain expertise, strong hands-on experience in US Healthcare is required
• Candidates should be able to understand healthcare business workflows and translate domain problems into technology solutions

Azure / Cloud

• Strong hands-on Microsoft Azure experience
• Azure cloud-native application development
• Azure services for application integration, data and AI
• API development and integration
• Cloud security, scalability and performance

Generative AI

• Hands-on experience building GenAI applications
• Strong experience implementing RAG (Retrieval Augmented Generation)
• Experience with one or more:
• LangChain
• LangGraph
• LlamaIndex
• Similar GenAI orchestration frameworks
• Experience with LLM APIs and enterprise GenAI integration
• Prompt engineering and context engineering
• Document/data ingestion and retrieval pipelines
• Vector search / embeddings concepts
• AI-powered application integration

Frontend

• React is preferred
• Strong Angular candidates can also be considered if they are willing to learn/work with React
• Experience integrating frontend applications with REST/AI APIs

Solutioning & Stakeholder Skills

• Strong ability to interact directly with business stakeholders and subject matter experts.
• Ability to understand business workflows and convert them into technical solutions.
• Strong problem-solving and solution-architecture mindset.
• Ability to communicate complex AI concepts to technical and non-technical stakeholders.
• Comfortable working in an ambiguous, fast-moving environment.
• Ability to take ownership from problem discovery through solution delivery.