Description

Responsibilities

 

• Partner directly with healthcare business stakeholders to understand workflows, challenges, and strategic objectives across payer operations.
• Translate business needs into scalable, secure, cloud native technology solutions.
• Design enterprise solution architecture and build end-to-end applications across frontend, backend, APIs, integrations, data platforms, and AI capabilities.
• Develop production grade software using .NET, Python, modern frameworks, microservices, APIs, and engineering best practices.
• Drive cloud engineering, DevOps, CI/CD, Infrastructure as Code, automation, monitoring, and operational excellence.
• Leverage AI/LLMs, intelligent agents, and automation to improve healthcare operations, engineering productivity, and business outcomes.
• Identify modernization opportunities and proactively recommend innovative solutions.
• Own the complete solution lifecycle—from discovery, architecture, design, development, testing, deployment, and production support.
• Mentor engineering teams and establish engineering excellence through architecture standards, coding practices, and innovation.
  
   

Domain Skills

 

• Strong expertise in healthcare payer/provider domains such as Member Enrollment & Eligibility, Claims Processing & Adjudication, Provider Management, Healthcare EDI (HIPAA X12 Transactions), Benefits Administration, Prior Authorization, Care Management (CM), Utilization Management (UM), Disease Management (DM), Clinical Operations, Grievance & Appeals (G&A), Payment Integrity.
• Experience with healthcare interoperability standards such as HL7/FHIR, APIs, and healthcare data exchange patterns is preferred.
  
   

Technical Skills

 

• Full Stack Engineering including fullstack application development, .NET / C#, Python, REST APIs & Microservices, Modern Frontend Frameworks, SQL / NoSQL databases, event driven architecture
• Cloud & DevOps expertise in Azure Cloud Architecture, cloud native application development, Azure DevOps / GitHub Actions, CI/CD automation, containers & Kubernetes, Infrastructure as Code, security, performance, and scalability engineering
• AI & Intelligent Engineering skills such as AI/LLM solution design and implementation, generative AI applications, agentic AI workflows, Retrieval Augmented Generation (RAG), intelligent automation, AI enabled software engineering practices

Key Skills

Education

Bachelor's degree