Senior Principal AI Engineer

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

• Experience leading complex technical initiatives and influencing architecture across cross-functional teams

• Strong communication skills with the ability to explain complex technical concepts to varied audiences

• Experience balancing experimentation speed with production engineering discipline, security, and maintainability

Technical Skills Required

• AI/ML platform architecture

• Generative AI systems and large language model integration

• Agentic workflows and orchestration frameworks

• Multi-agent or autonomous agent system design

• Prompt engineering and prompt management

• Workflow orchestration and automation

• Agent lifecycle management

• Model evaluation, benchmarking, and performance measurement

• AI observability, tracing, monitoring, and logging

• API design and service integration

• Distributed systems and scalable backend engineering

• Cloud platforms and cloud-native deployment patterns

• Productionization of AI/ML services

• Reliability, latency, throughput, and cost optimization

• CI/CD pipelines and DevOps/MLOps practices

• Secure software development and enterprise platform controls

Preferred Skills

• Advanced degree in Computer Science, Artificial Intelligence, Machine Learning, or a related discipline

• Experience with enterprise AI platforms, developer platforms, or internal tooling ecosystems

• Experience building frameworks or platforms that support bring-your-own-component or extensible developer patterns

• Demonstrated focus on developer experience, including designing self-service onboarding, SDKs, CLIs, sandboxes, templates, and documentation that reduce friction and accelerate time-to-first-deployment for internal builders

• Familiarity with model gateways, retrieval-augmented generation, and evaluation frameworks

• Experience implementing AI governance, responsible AI controls, and compliance-oriented technical solutions

• Knowledge of vector databases, knowledge retrieval systems, and orchestration layers for intelligent applications

• Experience in regulated industries such as biotechnology, pharmaceuticals, healthcare, or life sciences

• Strong mentoring and technical leadership experience in highly collaborative environments

• Ability to assess emerging AI technologies and translate them into practical platform capabilities