• Develop Software Implementation Designs – The Software Developer will create documentation that will explain the design of the software solution. • Develop Software Unit Tests – Create Unit Tests that prove the solution works as designed using Test Driven Development practices. • Create Software Installation Packages – Create installation packages that will allow our customers to install software on workstations and servers. • Create Software Implementations – Create source code that meets the needs of the design requirements by satisfying the rule established in a unit test. • Participate in Code Reviews – All code produced will be subject to a code review prior to check in to the source code control system. The Software Developer will be responsible for making modifications to the code based on the outcome of the code review. • Participate in Software Implementation Planning – The Software developer will participate in software planning sessions by contributing estimates of effort required to accomplish specific software design implementations. • Participate in Implementation Review Sessions – The Software Developer will participate in implementation review sessions. These sessions allow the developer to receive feedback from key stakeholders by demonstrating the software implementation. • Deliverables: Software source code software unit tests installation packages. • Architecture documents database scheme diagrams entity relationship diagrams sequence diagrams

• Design and implement multi-agent AI systems using frameworks such as LangChain, AutoGen, and CrewAI. • Build RAG (Retrieval-Augmented Generation) pipelines with vector databases like Pinecone or Weaviate. • Implement agent orchestration patterns and inter-agent communication protocols for distributed AI workflows. • Develop LLM fine-tuning workflows and optimize prompt engineering for production use. • Design and deploy Microsoft Copilot Foundry solutions with custom knowledge bases and integration workflows. • Develop backend services using C# /.NET (ASP.NET Core, Entity Framework, SignalR, Minimal APIs) and Python (FastAPI, Django, Flask). • Implement microservices and containerized deployments using Docker and event-driven architectures with Azure Service Bus or RabbitMQ. • Integrate Azure services including Azure Functions, Azure Logic Apps, Azure Cognitive Services, Azure OpenAI Service, and Azure Data Factory. • Build and maintain CI/CD pipelines and infrastructure as code using Azure DevOps, ARM/Bicep, or Terraform. • Mentor junior developers, document architecture decisions, and collaborate with product managers and data scientists. • Balance technical debt with feature delivery while considering scalability, maintainability, code quality, testing, and documentation. • Implement monitoring and observability using Application Insights. • Apply Git workflow management and branching strategies in collaborative development environments.

• Design, develop, and implement AI-powered features across healthcare product platforms and operational systems. • Build full-stack applications using modern AI engineering workflows and tools such as Cursor, Claude, Codex, OpenAI APIs, and related LLM technologies. • Collaborate with product managers, designers, engineers, and clinical stakeholders to deliver scalable, user-focused solutions that improve patient engagement, care coordination, and operational efficiency. • Develop APIs, backend services, automation pipelines, and AI workflows that support intelligent product capabilities and production-ready deployments. • Apply software engineering, DevOps, and MLOps best practices to testing, deployment, monitoring, and optimization of AI solutions. • Research emerging AI technologies, LLM implementation patterns, and modern product engineering frameworks to continuously improve platform capabilities. • Navigate ambiguity, solve complex technical challenges, and collaborate effectively across technical and non-technical teams in a fast-paced Agile environment. • Take on additional tasks and responsibilities as needed to support team objectives and ensure the success of the project.

• Design, develop, and implement AI-powered features across healthcare product platforms and operational systems. • Build full-stack applications using modern AI engineering workflows and tools such as Cursor, Claude, Codex, OpenAI APIs, and related LLM technologies. • Collaborate with product managers, designers, engineers, and clinical stakeholders to deliver scalable, user-focused solutions that improve patient engagement, care coordination, and operational efficiency. • Develop APIs, backend services, automation pipelines, and AI workflows that support intelligent product capabilities and production-ready deployments. • Apply software engineering, DevOps, and MLOps best practices to testing, deployment, monitoring, and optimization of AI solutions. • Research emerging AI technologies, LLM implementation patterns, and modern product engineering frameworks to continuously improve platform capabilities. • Navigate ambiguity, solve complex technical challenges, and collaborate effectively across technical and non-technical teams in a fast-paced Agile environment. • Take on additional tasks and responsibilities as needed to support team objectives and ensure the success of the project.