About this opportunity
Bank of America lists this Principal Engineer - Agentic AI Engineering opportunity in charlotte, North Carolina. Review the employer’s description below for duties, qualifications and application requirements.
Job description
Job Description:
At Bank of America, we are guided by a common purpose to help make financial lives better through the power of every connection. We do this by driving Responsible Growth and delivering for our clients, teammates, communities and shareholders every day.
Being a Great Place to Work and providing a culture of caring is core to how we drive Responsible Growth. We are intentional about fostering an inclusive workplace where every teammate has the opportunity to succeed, build a career and contribute to our shared success. This includes attracting and developing exceptional talent, recognizing and rewarding performance, and supporting our teammates’ physical, emotional, and financial wellness through affordable, competitive and flexible benefits.
We value the unique perspectives individuals bring from all backgrounds and career paths - whether shaped by military service, community college education, or a wide range of work and life experiences. These journeys foster resilience, leadership and innovation, strengthening our workforce and positively impacting the communities we serve.
Bank of America is committed to an in‑office culture that supports collaboration, engagement, and career development. Our approach includes clear in‑office expectations while providing an appropriate level of flexibility based on role‑specific responsibilities and business needs.
At Bank of America, you can build a successful career with opportunities to learn, grow, and make an impact. Join us!
Job Description:
This job is responsible for defining and leading the engineering approach for solutions at the program or portfolio level, to deliver significant business outcomes. Key responsibilities include continuously improving the design, quality, and reuse of the solution and delivering technology enablers that improve development efficiencies for the solution. Job expectations include familiarity with at least one area of engineering, acting as a “go to” reference across the organization, and applying knowledge to improve technical competencies through recruitment and development activities.
Developer Experience (DevEx) provides enterprise technical standards and common technical services, platforms, and tools that are leveraged by delivery teams across all lines of business. Within the SDLC Software Delivery Lifecycle program, this role leads portfolio product delivery strategy and execution for enterprise software delivery capabilities, ensuring the right investments, operating model, governance, and prioritization are in place to improve how internal technical users build, test, and deliver software at scale.
We are seeking a highly capable Agentic AI Engineering Principal Engineer to design, build, integrate, and support AI‑enabled engineering capabilities embedded across the Software Delivery Lifecycle (SDLC) . This role will help delivery teams adopt practical AI solutions that improve code authoring, test generation, automation, and developer productivity while aligning to enterprise engineering standards.
This role requires strong hands‑on engineering capability in one or more of GitHub Copilot, LangGraph, Semantic Kernel / Microsoft Agent Framework, SDLC automation, code and test generation, CI/CD integration, secure coding, and engineering productivity . The ideal candidate brings practical implementation experience, strong software engineering fundamentals, and a track record of turning AI‑assisted development capabilities into reliable, secure, and scalable delivery workflows.
Responsibilities:
Develops the engineering approach for the entire program/portfolio solution and works with Architecture, to develop/analyze/deliver the implementation of technical enablers
Leads the planning, definition, and design of the complex features which span multiple teams and explore solution alternatives
Creates ideas on designing complex technology and solution development approaches
Leads the technical oversight for teams in solution development including design reviews and code within own domain
Defines the technology tool stack for the solution within range of internally approved and supported technologies
Explores state‑of‑the‑art technologies to improve development efficiencies, quality of test/QA coverage, and release management
Leads and is responsible for the end‑to‑end test strategy/creation/adherence, and the integration between teams for a program/portfolio solution
Improve the experience for our developers, making it easier to deliver industry‑leading solutions, while managing work efficiently and with the right controls
Advance our technology platforms through innovation
Reduce risk and improve quality across our technology portfolio by aligning to a single enterprise architecture strategy and delivering governance that enables consistency, integration and automation
Required Qualifications:
Engineering Leadership & Enterprise Platforms
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
AI‑Assisted Engineering, SDLC Tooling & Automation
Hands‑on experience with GitHub Copilot and related AI‑assisted development workflows to improve code authoring, refactoring, documentation, and engineering efficiency
Practical knowledge of LangGraph and Semantic Kernel / Microsoft Agent Framework for building and integrating orchestrated AI workflows, tool connections, or engineering automation use cases
Experience implementing SDLC automation patterns that connect 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
Code & Test Generation, CI/CD Integration & Delivery Workflows
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 , with the 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
Secure Coding, Standards & Enterprise Enablement
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 ways that are supportable, governed, and maintainable
Familiarity with rollout patterns, onboarding, documentation, and developer enablement approaches that improve adoption and responsible use of AI‑assisted tooling
Implementation Impact, Adoption & Engineering Productivity
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
Education
Bachelor’s degree in computer science, Engineering, Information Systems, Applied Mathematics, or a related technical field
Desired Qualifications
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
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
Solution Delivery Process
Shift
1st shift (United States of America)
Hours Per Week
40
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Worksite address
charlotte, NC, 28245, US
Who can apply
Review the original listing for work authorization, qualifications and employer requirements.