Senior Principal Engineer, Agentic AI Platform, BYO Capability

Vertex Inc.•Boston, MA

1d•$188,000 - $282,000•Hybrid

About The Position

Vertex is seeking a Sr. Principal AI Engineer, Agentic AI Platform to design, build, and optimize the shared platform capabilities that power AI-enabled products and intelligent workflows across the enterprise. 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.

Requirements

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
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

Nice To Haves

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
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

Responsibilities

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
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