Principal Engineer - Agentic AI Engineering

Bank of America•Charlotte, NC

About The Position

The Principal Engineer leads the engineering approach for program- or portfolio-level solutions that deliver significant business outcomes. The role improves solution design, quality, reuse, and development efficiency by delivering technology enablers and providing technical leadership across teams. Within Developer Experience and the Software Delivery Lifecycle (SDLC) program, this role leads portfolio product delivery strategy and execution for enterprise software delivery capabilities. It helps establish the investments, operating model, governance, and prioritization needed to improve how internal technical users build, test, and deliver software at scale. The Agentic AI Engineering Principal Engineer designs, builds, integrates, and supports AI-enabled engineering capabilities across the SDLC. The role helps delivery teams use practical AI solutions for code authoring, test generation, automation, and developer productivity in alignment with enterprise engineering standards. It requires hands-on engineering expertise in areas such as GitHub Copilot, LangGraph, Semantic Kernel or Microsoft Agent Framework, SDLC automation, CI/CD integration, secure coding, and engineering productivity.

Requirements

7+ years of software engineering experience with hands-on delivery across enterprise platforms, developer tooling, automation, or AI-enabled engineering solutions.
Demonstrated experience implementing shared engineering capabilities, reusable automation patterns, or platform integrations used across multiple teams.
Experience engineering solutions in highly regulated environments with SDLC, risk, audit, and control requirements.
Ability to work with architects, platform teams, security partners, and delivery teams to translate standards into practical implementation patterns and working solutions.
Hands-on experience with GitHub Copilot and related AI-assisted development workflows for code authoring, refactoring, documentation, and engineering efficiency.
Practical knowledge of LangGraph and Semantic Kernel or Microsoft Agent Framework for orchestrated AI workflows, tool connections, or engineering automation use cases.
Experience implementing SDLC automation that connects AI-assisted capabilities to source control, build, test, release, and developer workflow systems.
Understanding of practical engineering productivity improvements enabled by AI, including reduced manual effort, faster iteration, and improved delivery consistency.
Experience using AI-assisted capabilities for code and test generation, including unit tests, test scaffolding, refactoring support, and developer-facing accelerators.
Strong foundation in CI/CD integration and ability to embed AI-enabled workflows into build, validation, pull request, release, and quality-control processes.
Ability to implement delivery workflows that balance automation speed with traceability, control, and supportability in enterprise engineering environments.
Hands-on collaboration with security, platform, and delivery teams to apply secure coding practices, review patterns, and controls to AI-assisted engineering workflows.
Strong understanding of enterprise engineering standards, practical guardrails, and implementation patterns that enable safe and consistent adoption of AI capabilities.
Experience integrating AI-assisted development capabilities with existing enterprise platforms and workflows in supportable, governed, and maintainable ways.
Familiarity with rollout patterns, onboarding, documentation, and developer enablement approaches that support adoption and responsible use of AI-assisted tooling.
Ability to implement reusable patterns, automation components, and developer enablement approaches that improve productivity, consistency, and speed to value.
Track record delivering engineering capabilities from pilot to adoption with measurable improvements in workflow efficiency, code quality, automation, and developer experience.
Success connecting AI-assisted engineering investments to reduced manual effort, improved test coverage, faster cycle times, and stronger delivery outcomes.
Experience evaluating implementation options, tool fitness, and workflow design choices to guide teams toward practical, scalable, and supportable engineering use cases.
Bachelor’s degree in computer science, Engineering, Information Systems, Applied Mathematics, or a related technical field.

Nice To Haves

Advanced degree in a technical discipline.
Equivalent record of senior engineering experience in developer tooling, software delivery automation, AI-assisted engineering, or enterprise platform integration.

Responsibilities

Develop the engineering approach for the entire program or portfolio solution and work with Architecture to develop, analyze, and deliver technical enablers.
Lead the planning, definition, and design of complex features spanning multiple teams, and explore solution alternatives.
Create approaches for designing complex technology and developing solutions.
Provide technical oversight to teams, including design reviews and code reviews within the role’s domain.
Define the solution’s technology tool stack using internally approved and supported technologies.
Explore technologies that can improve development efficiency, test and QA coverage, and release management.
Lead and be accountable for end-to-end test strategy, creation, adherence, and integration between teams for a program or portfolio solution.
Improve the developer experience so teams can deliver solutions more easily while managing work efficiently and with appropriate controls.
Advance technology platforms through innovation.
Reduce risk and improve portfolio quality by aligning to enterprise architecture strategy and delivering governance that enables consistency, integration, and automation.
Help delivery teams adopt AI-enabled capabilities for code authoring, test generation, automation, and developer productivity.