Principal Engineer - Agentic AI Engineering
Bank of America · Charlotte, NC · 2 wk ago
EngineeringFull-time
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 a 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
Requirements
- 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
Qualifications
- Bachelor’s degree in computer science, Engineering, Information Systems, Applied Mathematics, or a related technical field
- 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 (desired)
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