Senior Principal AI Engineer

Job Description Vertex is seeking a   Senior   Principal AI Engineer   to design, build, and optimize the shared platform capabilities that power AI-enabled products and intelligent workflows across the enterprise.   Working within the Agentic AI Platform team, this   role will focus on delivering production-grade platform services for model integration, prompt and workflow orchestration, evaluation, observability, performance optimization, and agent lifecycle management.   A key focus of this role will be enabling a build/bring-your-own-agents capability within the Agentic AI Platform, allowing teams across Vertex to create, integrate, customize, and operationalize their own agents using shared platform standards, tooling, and governance controls.   The ideal candidate combines strong software engineering fundamentals with deep experience in applied AI systems. This individual will be comfortable operating across rapid experimentation and engineering rigor, translating emerging AI capabilities into scalable, reliable, secure, and reusable platform components. The Senior Principal AI Engineer will play a critical leadership role in accelerating AI adoption across Vertex by enabling product teams to build and deploy AI solutions faster and more effectively.   Key Responsibilities   Build/bring-your-own-agents capability (primary focus): the frameworks, SDKs, templates, interfaces, and guardrails that let teams across Vertex create, integrate, customize, and operationalize their own agents on shared platform standards   Platform services: model integration, prompt and workflow orchestration, tool use, memory patterns, and agentic task coordination that other teams build against   Agent lifecycle and quality: registration, configuration, testing, deployment, versioning, monitoring, and retirement, plus the evaluation and benchmarking frameworks behind them   Developer experience: self-service onboarding, documentation, reference implementations, and enablement resources that shorten the path from idea to production   Standards and technical leadership: platform APIs, service contracts, architecture patterns, and the engineering practices that keep custom agents safe, reliable, and supportable   Architect and develop shared AI/agentic platform services that support enterprise AI products and internal workflows   Design and implement a build/bring-your-own-agents capability that enables teams to create, register, integrate, deploy, and manage their own agents within the enterprise agentic platform   Establish reusable frameworks, SDKs, templates, interfaces, and guardrails that standardize how custom agents are built and onboarded onto the platform   Own the   developer experience   for the platform, delivering intuitive self-service onboarding, SDKs, CLIs, sandbox environments, reference implementations, and clear documentation that let builders move from idea to production quickly   Define agent lifecycle capabilities including agent registration, configuration, testing, deployment, monitoring, versioning, and retirement   Build and maintain robust integrations with foundation models, model gateways, APIs, enterprise tools, and related AI infrastructure   Design and implement systems for prompt orchestration, workflow execution, tool use, memory patterns, and agentic task coordination   Develop reusable frameworks and services for evaluation, benchmarking, and validation of AI model, agent, and workflow performance   Establish platform capabilities for observability, monitoring, tracing, logging, and alerting across AI workloads and autonomous agent interactions   Optimize platform performance, scalability, latency, reliability, and cost efficiency for production AI and agentic systems   Partner with product, data, engineering, security, and architecture teams to enable enterprise-ready AI solutions   Translate prototypes and experimental concepts into hardened, maintainable, production-grade services   Define engineering standards, best practices, and design patterns for AI platform development and deployment   Support governance, risk management, and responsible AI practices through measurable controls, policy enforcement, and technical safeguards for agent behavior   Drive platform adoption by creating reusable components, documentation, onboarding patterns, and developer enablement resources   Mentor engineers and provide technical leadership across AI platform initiatives   Evaluate emerging tools, frameworks, and architectural patterns in generative AI and agentic systems to inform platform strategy   Required Qualifications   Bachelor’s degree in Computer Science , Software Engineering, Machine Learning, Data Engineering, or a related technical field; advanced degree preferred   Significant industry experience in software engineering, machine learning engineering, or AI platform development, including experience in senior or principal-level technical roles   Proven track record designing and delivering production-scale AI or ML platforms   Strong experience building distributed systems, APIs, microservices, and cloud-native applications   Demonstrated experience operationalizing machine learning, generative AI, or agent-based solutions in enterprise environments   Experience designing extensible platform capabilities that enable internal teams to build or integrate custom applications, tools, or services   Deep understanding of software engineering best practices including testing, CI/CD, version control, code review, and system reliability   Deep understanding of AI-native software engineering practices and experience establishing standards, governance, and best practices for the responsible use of AI coding assistants and software engineering agents across engineering teams   Experience defining architecture, standards, and reusable services for large-scale enterprise environments