Principal Engineer - Agentic AI Platform Engineering & Performance

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
• 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
• Define how agentic AI systems operate efficiently at scale, ensuring performance, cost, and reliability are engineered as first-class concerns

Requirements

• 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
• 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
• Proven ability to establish observability and SLAs for AI systems, including token usage, latency, cost, and quality metrics
• 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
• Proven ability to establish governance models ensuring efficient and sustainable scaling of agentic workloads
• Proven ability to connect platform investments to improved delivery speed, quality, resilience, and cost efficiency

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 distinguished technical leadership in AI platforms, developer tooling, or software delivery engineering