Principal Engineer - Agentic AI Platform Engineering & Performance

Bank of America•Charlotte, NC

1d•Onsite

About The Position

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. Within DevEx we are seeking a highly accomplished Agentic AI Platform Engineering & Performance Principal Engineer to define and drive the architecture, engineering standards, and technical roadmap for AI capabilities embedded across the SDLC. This role will shape how AI is operationalized across developer workflows, enabling scalable, secure, and governed adoption of agentic AI capabilities across engineering teams. This role requires deep technical leadership in agent orchestration, LLMOps pipelines, runtime governance, AI performance engineering, and developer tooling. The ideal candidate brings strong hands-on engineering depth, platform design expertise, and experience building reusable AI capabilities that are scalable, observable, cost-efficient, and performant. This role leads engineering direction for AI-enabled delivery capabilities that improve how software is designed, built, tested, governed, and operated at scale. This role advances developer productivity, platform innovation, and enterprise consistency while reducing risk and improving quality through aligned architecture, governance, and automation. It also defines how agentic AI systems operate efficiently at scale, ensuring performance, cost, and reliability are engineered as first-class concerns.

Requirements

15+ years of engineering experience with deep technical leadership in enterprise platforms, developer tooling, or AI-enabled engineering systems
Demonstrated ownership of architecture, standards, and engineering direction for shared platforms across multiple lines of business
Experience operating in highly regulated environments with strong SDLC, risk, and audit requirements
Ability to influence senior technology leaders and stakeholders through clear technical strategy and engineering standards
Deep expertise in enterprise AI platforms, including agentic architectures, orchestration frameworks, and reusable service patterns
Strong command of LLMOps pipelines, including prompt and model versioning, evaluation frameworks, testing automation, and release lifecycle management
Proven ability to establish runtime governance and performance optimization, including: Policy enforcement, observability, resiliency, and safe execution controls
Latency optimization, token efficiency, and cost-aware execution of AI workflows
Intelligent orchestration strategies balancing quality, cost, and responsiveness
Experience building AI developer tooling integrated with SDLC workflows, including assistants, test generation, and evaluation harnesses
Hands-on knowledge of secure integration patterns across CI/CD, source control, and enterprise developer platforms
Deep expertise in performance engineering of LLM and agentic systems, including latency profiling, throughput optimization, and scalable execution
Strong understanding of token optimization strategies, including: Prompt compression and structured prompting, Context window management and dynamic context injection, Minimizing token usage while preserving output quality
Experience designing cost-efficient AI systems, including: Token usage telemetry and cost-per-transaction modeling, Budget controls, throttling, and multi-model routing strategies
Proven ability to implement runtime optimizations such as: Caching (prompt, response, embeddings), Context pruning and retrieval optimization (RAG), and Parallelized agent workflows and efficient tool invocation
Experience establishing observability and SLAs for AI systems, including token usage, latency, cost, and quality metrics
Experience building platforms that improve developer productivity and standardize delivery across build, test, and release workflows
Strong understanding of CI/CD, platform engineering practices, and AI integration into enterprise delivery ecosystems
Ability to create reusable patterns, guardrails, and self-service capabilities that accelerate teams without compromising control
Experience embedding governance and control requirements into AI platforms in partnership with Security, Risk, and Compliance teams
Strong foundation in identity-aware access, data protection, and model/prompt governance
Experience with threat modeling, secure design, and enterprise AI governance frameworks
Ability to define platform operating models, standards, and adoption strategies for AI capabilities across the SDLC.
Proven success scaling platforms from incubation to enterprise adoption with measurable impact
Demonstrated ability to define performance SLAs (latency, cost, token usage) for enterprise AI systems
Demonstrated ability to establish governance models ensuring efficient and sustainable scaling of agentic workloads
Demonstrated ability to connect platform investments to improved delivery speed, quality, resilience, and cost efficiency
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 distinguished technical leadership in AI platforms, developer tooling, or software delivery engineering

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 ranged 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