Goods & Services - Senior Software & AI Modernization Engineer
Requirements
• Solid enterprise software development experience oriented to JavaScript, TypeScript, GO, and Python. • Modern application architecture • Legacy application modernization • API design and integration • REST APIs and web services • SQL and relational database experience • Object-oriented application development • Git/source control • CI/CD and application deployment • Application troubleshooting and root cause analysis • Testing and release management • Secure application development • Experience integrating third-party systems and services • AI application development • AI agents, including Github Copilot, Cursor, Cloud code and Codex • Prompt engineering • Tool/function calling • Retrieval-Augmented Generation (RAG) • Vector search / embeddings • AI workflow automation • Enterprise AI integration • AI evaluation and validation • Microsoft/Azure AI technologies • Preferred: • Microsoft technology environment • Azure/ AWS instead • Enterprise application modernization • Healthcare or skilled nursing environments • Internal business applications • Large legacy application conversions • Application and database integrations, such as Good Dynamo DB, Mongo DB, Aurora DB • Automation platforms, such as Jenkins • Microsoft Power Platform / Copilot ecosystem
Responsibilities
• Current business functionality • Existing architecture • Database dependencies • External integrations • Authentication and security dependencies • Reports and downstream consumers • Unsupported or aging technologies • Production dependencies • Then design and execute a controlled path toward the new architecture without disrupting existing operations. • Modernization should not simply mean rewriting code. The developer must understand what the existing system does, why it does it, and what must be preserved before replacing it. • Build AI-enabled internal tools and business applications. • Work with LLM APIs and AI platforms. • Develop AI agents and workflow automation where appropriate. • Implement structured prompting, tool/function calling, APIs, and application integration. • Connect AI solutions to approved enterprise data sources. • Design appropriate validation and human review where AI output affects business decisions. • Evaluate AI output quality and reliability. • Implement appropriate logging, monitoring, access control, and failure handling. • Move AI initiatives from prototype into supportable production solutions.
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