Manager - Software Engineer (Agentic Engineering) - Tax Technology

Overview In this role you lead a small engineering team to build an agentic software development platform. You own architecture and delivery of LLM Wiki and knowledge bases on AWS Bedrock, enabling agentic tooling for dev teams. You design scalable, secure AI solutions with robust observability and cloud deployment, and integrate development tools to automate artifact-driven workflows. You mentor others while shaping the platform’s technical direction in a fast-evolving transformation program. ResponsibilitiesPlatform ownership of the LLM Wiki and knowledge bases for steering contentDesign and deliver agentic AI solutions with retrieval-augmented generation, tool calling, and stateful workflowsEnsure alignment with enterprise architecture, security, compliance, and operational standardsDevelop and maintain integrations across AWS Kiro, Azure DevOps, and ServiceNowBuild tools and workflows to help program managers identify risk and cross-project dependencies Key requirementsBachelor’s degree in computer science, information technology, software engineering, or a related field5+ years of experience with modern programming languages and cloud-native platforms (e.g., Python, Node.js, TypeScript, SQL/NoSQL, AWS)Strong solution design and architecture capabilities with agentic AI patterns, LLM evaluation, retrieval-augmented generation, tool calling, and observabilityHands-on experience with large language model tooling and knowledge bases (AWS Bedrock or equivalent)Experience building integrations between enterprise tools (Azure DevOps, ServiceNow, Jira, Confluence) via APIsExperience delivering automated CI/CD pipelines within Agile environmentsAbility to travel up to 10%Active accreditations as listed (CPA, attorney, Enrolled Agent, CISSP, etc.)Preferred: experience with agentic development tools (AWS Kiro, GitHub Copilot) and knowledge-base design for retrieval-augmented generationStrong collaboration and relationship-buildingClear, concise communication with technical and non-technical audiencesAdaptability and continuous learningPython and cloud-native development (AWS)LLM patterns: retrieval-augmented generation, tool calling, stateful workflowsObservability and security/compliance in cloud deployments