Principal Engineer - Agentic AI Engineering

Bank of America•Charlotte, NC

About The Position

The Principal Engineer defines and 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 through technology enablers, while serving as an organizational reference and contributing to technical competency through recruitment and development. Within Developer Experience (DevEx) and the Software Delivery Lifecycle (SDLC) program, this role leads portfolio product delivery strategy and execution for enterprise software delivery capabilities. It helps ensure appropriate investments, operating models, governance, and prioritization so internal technical users can build, test, and deliver software at scale. The Agentic AI Engineering Principal Engineer designs, builds, integrates, and supports AI-enabled engineering capabilities embedded across the SDLC. The role helps delivery teams adopt practical AI solutions for code authoring, test generation, automation, and developer productivity while aligning with enterprise engineering standards. It calls for hands-on engineering capability in areas such as GitHub Copilot, LangGraph, Semantic Kernel/Microsoft Agent Framework, SDLC automation, code and test generation, CI/CD integration, secure coding, and engineering productivity. Bank of America describes an in-office culture supporting collaboration, engagement, and career development, with flexibility based on role-specific responsibilities and business needs. The position is first shift and is listed as 40 hours per week.

Requirements

At least 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 strong SDLC, risk, audit, and control requirements.
Ability to work effectively 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/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.
Strong 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 balancing 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 improve 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.
Proven track record delivering engineering capabilities from pilot to adoption through measurable improvements in workflow efficiency, code quality, automation, and developer experience.
Demonstrated 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 or equivalent record of senior engineering experience in developer tooling, software delivery automation, AI-assisted engineering, or enterprise platform integration.
Skills listed: Automation, Influence, Result Orientation, Stakeholder Management, Technical Strategy Development, Application Development, Architecture, Business Acumen, Risk Management, Solution Design, Agile Practices, Analytical Thinking, Collaboration, Data Management, and Solution Delivery Process.

Responsibilities

Develop the engineering approach for the entire program or portfolio solution and work with Architecture to develop, analyze, and deliver technical enablers.
Lead planning, definition, and design of complex features spanning multiple teams, and explore solution alternatives.
Create approaches for complex technology design and solution development.
Provide technical oversight for teams developing solutions, including design reviews and code within the role’s domain.
Define the solution technology stack from internally approved and supported technologies.
Explore state-of-the-art technologies to improve development efficiency, test/QA coverage, and release management.
Lead and own end-to-end test strategy, creation, adherence, and integration between teams for a program or portfolio solution.
Improve developer experience and help developers deliver solutions efficiently with appropriate controls.
Advance technology platforms through innovation.
Reduce risk and improve quality across the technology portfolio by aligning with enterprise architecture and delivering governance that enables consistency, integration, and automation.