Senior AI Agentic Engineer

About the role

The Senior AI Agentic Engineer is a hands-on software engineer responsible for designing, building, deploying, and supporting production-grade AI agents and intelligent automation solutions within the Truist Agentic Enterprise (TAE). This role combines strong software engineering fundamentals with expertise in modern AI technologies, including large language models (LLMs), agent frameworks, orchestration patterns, retrieval systems, and enterprise integrations. The engineer develops scalable, secure, and reliable solutions that combine AI capabilities, business logic, APIs, data services, and workflow automation to solve real business problems. Working as a senior individual contributor, the role independently delivers well-defined initiatives, contributes to engineering standards and code quality, and helps increase team delivery velocity through technical leadership, code reviews, and reusable implementation patterns. The ideal candidate brings deep Python expertise, strong engineering discipline, experience building AI-enabled applications, and a commitment to delivering enterprise-ready solutions that are observable, testable, maintainable, and governed.

Responsibilities

• Design, develop, deploy, and support production-ready AI agents, agentic workflows, and intelligent automation solutions.
• Build solutions that leverage LLMs, orchestration frameworks, retrieval systems, APIs, business services, and enterprise data sources to deliver measurable business value.
• Implement agent workflows using established engineering patterns, including tool orchestration, workflow routing, human-in-the-loop controls, session management, and safe fallback mechanisms.
• Develop and maintain integrations with enterprise applications, services, APIs, and governed data platforms.
• Design and optimize prompts, tool definitions, retrieval strategies, context management approaches, and execution workflows to improve solution quality, reliability, and performance.
• Contribute to retrieval-augmented generation (RAG), knowledge grounding, search, vectorization, and information retrieval capabilities.
• Implement reliability, resiliency, and safety controls, including exception handling, monitoring, guardrails, validation logic, and escalation workflows.
• Develop automated testing, evaluation, and validation capabilities to ensure solution quality across releases, model updates, prompt changes, and workflow enhancements.
• Instrument solutions with telemetry, logging, and observability capabilities to support operational monitoring and troubleshooting.
• Participate in code reviews, contribute to engineering standards, and promote software engineering best practices across the organization.
• Create and maintain technical documentation, deployment procedures, operational runbooks, and support materials.
• Collaborate with product, architecture, platform, security, data, and quality engineering teams to deliver secure and scalable AI solutions.
• Contribute reusable components, reference implementations, and engineering patterns that improve platform adoption and team productivity.

Requirements

• Bachelor’s