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.
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
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
Required Qualifications:Engineering Leadership & Enterprise Platforms- 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
AI Platform Engineering, Agent Orchestration & LLMOps- 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
Agentic AI Performance Engineering & Optimization- 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
SDLC Platform Engineering & Developer Enablement- 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
Security, Risk & Compliance- 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
Platform Adoption, Operating Model & Engineering Impact- 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
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 distinguished technical leadership in AI platforms, developer tooling, or software delivery engineering
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
