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This is a great opportunity for an experienced senior-level software engineer to use their experience with generative AI to design, build, and scale AI‑driven applications that deliver measurable business outcomes. This role is a blend of hands-on architecture, development, coaching and mentoring, technical leadership, client-facing execution, and a strong emphasis on Azure-based AI platforms, LLM integrations, and agentic systems for fast AI experimentation pilots. If you are a software engineering professional who is on the leading edge of AI and building efficiency, this is a great opportunity.
Required Skills:
Must have 10+ years’ experience in full stack software engineering
Must be able to serve as a SME across an organization’s AI tools and services
Must be able to design, develop, deploy, and maintain enterprise AI applications, including conversational interfaces, knowledge graphs, retrieval-augmented generation (RAG), and agentic workflows
Must be able to use AI-native development for coding, testing, debugging, and documentation while independently validating generated artifacts and maintaining engineering standards
Must have strong Python and React experience as well as experience with SQL, API’s and modern application architecture
Experience with Node.js, C#/.NET, Javascript/Typescript, and FastAPI are ideal but not required
Experience with use of GitHub Copilot and approved coding assistants, including repository context, test generation, code review, debugging, and validation of generated implementations is ideal
Experience with Azure AI Foundry, Azure OpenAI, Azure AI Search, and Azure Document Intelligence is ideal along with practical experience integrating model services into applications
Must have experience with prompt engineering, document ingestion/extraction, RAG, tool integration, and multi-step agentic workflows
Must have experience with evaluating datasets, prompt regression testing, tracing, and quality/safety measurement is ideal
Familiarity with Foundry Evaluators, RAGAS, or comparable approaches is ideal
Experience with Azure, Azure Functions, Git, CI/CD, infrastructure as code/Terraform, containers/Docker, and Agile delivery practices is ideal
Experience with REST APIs, identity/access controls, OAuth/JWT or Entra-based authentication, and secure data handling is ideal
Must have experience delivering multiple AI use cases into production at scale
Must have a solid understanding and experience with generative AI in the software development space
Must be able to architect and deliver production-grade LLM-powered applications, including chat, document intelligence, and agentic workflows
Must be able to mentor onshore and offshore engineering teams on use of AI to improve code quality, architecture discipline, and delivery velocity
Must be able to design scalable, secure, cloud-native systems using Azure, Docker, Kubernetes, and event-driven architectures
Experience with agent frameworks such as LangGraph, Semantic Kernel, or DSPy, and agent tracing/evaluation tools is a plus
Must be able to design and implement prompt engineering, evaluation frameworks, and observability for AI systems
Must be able to ensure enterprise standards for authentication, authorization, and system integration
Must be able to collaborate on requirements gathering, solution design, and development with other IT app dev and business stakeholders
Must be able to translate business problems into AI-enabled and agentic solutions
Must be able to design systems capable of processing high-volume documents and transactions with accuracy and traceability
Must be able to continuously iterate solutions based on real-world usage and feedback
Must be a self-starter who can work independently but also with a team with little oversite
Must be able communicate with various IT teams as well as business stakeholders on occasion
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