Senior Principal AI Engineer
Vertex Pharmaceuticals
Senior Principal AI Engineer
Job Description
Job DescriptionVertex is seeking a Senior Principal AI Engineer to define and build the foundational enterprise AI platform that powers intelligent applications across the enterprise. This role will lead the design and implementation of scalable, secure, and reusable capabilities for agentic AI, with a strong focus on retrieval-augmented generation (RAG), orchestration frameworks, evaluation systems, and platform architecture. In addition to building out the core platform, this engineer will own the vision and day-to-day operations of a centralized AI Gateway / Control Plane / Control Tower that enables agent monitoring, observability, policy enforcement, governance, and operational controls across AI solutions at Vertex. This is a highly strategic and hands-on role for an experienced AI engineer who is excited to shape enterprise AI architecture, standards, and long-term technical direction. Key Responsibilities Agentic AI platform: the shared architecture, services, and reusable capabilities that make it faster and safer to build AI-powered applications across Vertex AI Control Tower operations: standing up and running the centralized control plane for agent monitoring, observability, telemetry, policy enforcement, guardrails, usage analytics, and auditability Own the strategy, architecture, implementation, and day-to-day operation of the centralized AI Gateway / Control Tower as the enterprise control point for model access, routing, governance, cost management, and operational oversight Intelligent model routing, provider abstraction, fallback, failover, rate limiting, and workload optimization across approved models AI FinOps, including token and consumption visibility, budgeting, chargeback/showback, cost allocation, forecasting, and model-cost optimization Centralized guardrails for content safety, prompt-injection defense, data-loss prevention, sensitive-data handling, and responsible AI policy enforcement Identity, access, security, privacy, regulatory compliance, and lifecycle governance controls for models, agents, tools, and AI interactions End-to-end observability, telemetry, quality monitoring, latency and reliability metrics, incident response, and operational health management Usage analytics, immutable audit trails, policy evidence, risk reporting, and executive-level transparency across the enterprise AI estate Model onboarding, approval, versioning, deprecation, resiliency, capacity management, and third-party provider governance Retrieval and knowledge systems: RAG pipelines, vector search, document retrieval, and the grounding patterns that make enterprise content usable by agents Agent lifecycle and quality: deployment, versioning, evaluation frameworks, reliability measurement, and continuous improvement of agents in production Standards and technical leadership: platform APIs, service contracts, architecture patterns, and the engineering practices other teams build against Define the technical vision, architecture, and roadmap for Vertex’s enterprise agentic AI platform Design and build reusable platform services that accelerate development of safe, reliable, and scalable AI-powered applications Lead architecture and implementation for RAG pipelines, knowledge retrieval systems, prompt workflows, tool use, and agent orchestration Establish core frameworks for agent lifecycle management, including deployment, monitoring, observability, evaluation, and continuous improvement Develop scalable infrastructure patterns for enterprise AI workloads, including model integration, data access, and orchestration services Partner closely with product, engineering, data, security, and UX teams to deliver common AI platform capabilities that support multiple use cases across Vertex Establish best practices for AI reliability, evaluation, safety, and performance measurement Drive technical standards for platform APIs, service contracts, architecture patterns, and reusable components Evaluate emerging technologies, frameworks, and vendors in the AI/agentic ecosystem and make strategic recommendations Mentor engineers and influence cross-functional technical teams through architectural leadership and hands-on guidance Ensure platform solutions align with enterprise requirements for scalability, resilience, security, and maintainability Contribute to Vertex’s long-term AI strategy by identifying opportunities to expand platform capabilities and increase enterprise adoption Required Qualifications Advanced degree in Computer Science, Engineering, Artificial Intelligence, Machine Learning, or a related technical field; or equivalent combination of education and experience 10+ years of experience designing and building enterprise-grade AI/ML platforms and distributed systems Deep expertise in agentic AI architectures, LLM-based applications, and platform engineering Proven experience with retrieval-augmented generation (RAG) systems, vector search, document retrieval, and knowledge integration patterns Strong experience with AI orchestration frameworks, workflow engines, and multi-step agent execution patterns Demonstrated experience designing centralized operational platforms for monitoring, governance, observability, and control Demonstrated experience using AI-assisted software development and autonomous coding agents to design, generate, test, review, debug, optimize, and refactor code across complex enterprise systems. 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 Strong understanding of AI system evaluation, quality measurement, and performance optimization Experience partnering with cross-functional stakeholders and influencing technical direction across teams Excellent communication, leadership, and problem-solving skills Ability to balance strategic architecture leadership with hands-on technical execution Technical Skills Required Agentic AI platform architecture Large Language Models (LLMs) Retrieval-Augmented Generation (RAG) AI orchestration and workflow design Agent monitoring and observability AI governance, policies, and controls AI FinOps, token economics, budgeting, cost allocation, consumption forecasting, and model-cost optimization AI Gateway / Control Tower architecture, including model routing, provider abstraction, fallback, rate limiting, and policy enforcement Evaluation frameworks for AI systems Platform engineering and reusable service design Distributed systems architecture API and service design Knowledge retrieval systems Telemetry, logging, and operational analytics Scalability, reliability, and performance engineering Security and enterprise controls for AI platforms Preferred Skills Experience building enterprise AI platforms in regulated or highly governed environments Familiarity with human-in-the-loop workflows and responsible AI practices Experience implementing policy engines, guardrails, and audit frameworks for AI applications Knowledge of ML infrastructure, model serving, and production AI operations Experience with cloud-native architectures and modern DevOps/MLOps practices Exposure to user experience considerations for AI-powered applications and intelligent systems Ability to translate complex technical capabilities into scalable enterprise adoption strategies Experience leading technical teams through platform transformation initiatives #LI-HYBRIDPay Range:$188,000 - $282,000Disclosure Statement:The range provided is based on what we believe is a reasonable estimate for the base salary pay range for this job at the time of posting. This role is eligible for an annual bonus and annual equity awards. Some roles may also be eligible for overtime pay, in accordance with federal and state requirements. Actual base salary pay will be based on a number of factors, including skills, competencies, experience, and other job-related factors permitted by law.At Vertex, our Total Rewards offerings also include inclusive market-leading benefits to meet our employees wherever they are in their career, financial, family and wellbeing journey while providing flexibility and resources to support their growth and aspirations. From medical, dental and vision benefits to generous paid time off (including a week-long company shutdown in the Summer and the Winter), educational assistance programs including student loan repayment, a generous commuting subsidy, matching charitable donations, 401(k) and so much more.Company InformationVertex is a global biotechnology company that invests in scientific innovation. Vertex is committed to equal employment opportunity and non-discrimination for all employees and qualified applicants without regard to a person's race, color, sex, gender identity or expression, age, religion, national origin, ancestry, ethnicity, disability, veteran status, genetic information, sexual orientation, marital status, or any characteristic protected under applicable law. Vertex is an E-Verify Employer in the United States. Vertex will make reasonable accommodations for qualified individuals with known disabilities, in accordance with applicable law. Any applicant requiring an accommodation in connection with the hiring process and/or to perform the essential functions of the position for which the applicant has applied should make a request to the recruiter or hiring manager, or contact Talent Acquisition at ApplicationAssistance@vrtx.com
Locations
- MA, Boston
Salary
188,000 - 282,000 USD / yearly
Skills Required
- agentic AI architecturesintermediate
- retrieval-augmented generationintermediate
- AI orchestration frameworksintermediate
- AI-assisted software developmentintermediate
- enterprise AI platforms in regulatedintermediate
- human-in-the-loop workflowsintermediate
- ML infrastructureintermediate
- cloud-native architecturesintermediate
Required Qualifications
- Advanced degree in Computer Science, Engineering, Artificial Intelligence, Machine Learning, or a related technical field; or equivalent combination of education and experience (experience)
- Advanced degree in Computer Science, Engineering, Artificial Intelligence, Machine Learning, or a related technical field; or equivalent combination of education and experience (experience)
- 10+ years of experience designing and building enterprise-grade AI/ML platforms and distributed systems (experience, 10 years)
- 10+ years of experience designing and building enterprise-grade AI/ML platforms and distributed systems (experience, 10 years)
- Deep expertise in agentic AI architectures, LLM-based applications, and platform engineering (experience)
- Deep expertise in agentic AI architectures, LLM-based applications, and platform engineering (experience)
- Proven experience with retrieval-augmented generation (RAG) systems, vector search, document retrieval, and knowledge integration patterns (experience)
- Proven experience with retrieval-augmented generation (RAG) systems, vector search, document retrieval, and knowledge integration patterns (experience)
- Strong experience with AI orchestration frameworks, workflow engines, and multi-step agent execution patterns (experience)
- Strong experience with AI orchestration frameworks, workflow engines, and multi-step agent execution patterns (experience)
- Demonstrated experience designing centralized operational platforms for monitoring, governance, observability, and control (experience)
- Demonstrated experience designing centralized operational platforms for monitoring, governance, observability, and control (experience)
- Demonstrated experience using AI-assisted software development and autonomous coding agents to design, generate, test, review, debug, optimize, and refactor code across complex enterprise systems. (experience)
- Demonstrated experience using AI-assisted software development and autonomous coding agents to design, generate, test, review, debug, optimize, and refactor code across complex enterprise systems. (experience)
- 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)
- 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)
- Experience defining architecture, standards, and reusable services for large-scale enterprise environments (experience)
- Experience defining architecture, standards, and reusable services for large-scale enterprise environments (experience)
- Strong understanding of AI system evaluation, quality measurement, and performance optimization (experience)
- Strong understanding of AI system evaluation, quality measurement, and performance optimization (experience)
- Experience partnering with cross-functional stakeholders and influencing technical direction across teams (experience)
- Experience partnering with cross-functional stakeholders and influencing technical direction across teams (experience)
- Excellent communication, leadership, and problem-solving skills (experience)
- Excellent communication, leadership, and problem-solving skills (experience)
- Ability to balance strategic architecture leadership with hands-on technical execution (experience)
- Ability to balance strategic architecture leadership with hands-on technical execution (experience)
- Agentic AI platform architecture (experience)
- Agentic AI platform architecture (experience)
- Large Language Models (LLMs) (experience)
- Large Language Models (LLMs) (experience)
- Retrieval-Augmented Generation (RAG) (experience)
- Retrieval-Augmented Generation (RAG) (experience)
- AI orchestration and workflow design (experience)
- AI orchestration and workflow design (experience)
- Agent monitoring and observability (experience)
- Agent monitoring and observability (experience)
- AI governance, policies, and controls (experience)
- AI governance, policies, and controls (experience)
- AI FinOps, token economics, budgeting, cost allocation, consumption forecasting, and model-cost optimization (experience)
- AI FinOps, token economics, budgeting, cost allocation, consumption forecasting, and model-cost optimization (experience)
- AI Gateway / Control Tower architecture, including model routing, provider abstraction, fallback, rate limiting, and policy enforcement (experience)
- AI Gateway / Control Tower architecture, including model routing, provider abstraction, fallback, rate limiting, and policy enforcement (experience)
- Evaluation frameworks for AI systems (experience)
- Evaluation frameworks for AI systems (experience)
- Platform engineering and reusable service design (experience)
- Platform engineering and reusable service design (experience)
- Distributed systems architecture (experience)
- Distributed systems architecture (experience)
- API and service design (experience)
- API and service design (experience)
- Knowledge retrieval systems (experience)
- Knowledge retrieval systems (experience)
- Telemetry, logging, and operational analytics (experience)
- Telemetry, logging, and operational analytics (experience)
- Scalability, reliability, and performance engineering (experience)
- Scalability, reliability, and performance engineering (experience)
- Security and enterprise controls for AI platforms (experience)
- Security and enterprise controls for AI platforms (experience)
Preferred Qualifications
- Experience building enterprise AI platforms in regulated or highly governed environments (experience)
- Experience building enterprise AI platforms in regulated or highly governed environments (experience)
- Familiarity with human-in-the-loop workflows and responsible AI practices (experience)
- Familiarity with human-in-the-loop workflows and responsible AI practices (experience)
- Experience implementing policy engines, guardrails, and audit frameworks for AI applications (experience)
- Experience implementing policy engines, guardrails, and audit frameworks for AI applications (experience)
- Knowledge of ML infrastructure, model serving, and production AI operations (experience)
- Knowledge of ML infrastructure, model serving, and production AI operations (experience)
- Experience with cloud-native architectures and modern DevOps/MLOps practices (experience)
- Experience with cloud-native architectures and modern DevOps/MLOps practices (experience)
- Exposure to user experience considerations for AI-powered applications and intelligent systems (experience)
- Exposure to user experience considerations for AI-powered applications and intelligent systems (experience)
- Ability to translate complex technical capabilities into scalable enterprise adoption strategies (experience)
- Ability to translate complex technical capabilities into scalable enterprise adoption strategies (experience)
- Experience leading technical teams through platform transformation initiatives (experience)
- Experience leading technical teams through platform transformation initiatives (experience)
Responsibilities
- Agentic AI platform: the shared architecture, services, and reusable capabilities that make it faster and safer to build AI-powered applications across Vertex
- Agentic AI platform: the shared architecture, services, and reusable capabilities that make it faster and safer to build AI-powered applications across Vertex
- AI Control Tower operations: standing up and running the centralized control plane for agent monitoring, observability, telemetry, policy enforcement, guardrails, usage analytics, and auditability
- AI Control Tower operations: standing up and running the centralized control plane for agent monitoring, observability, telemetry, policy enforcement, guardrails, usage analytics, and auditability
- Own the strategy, architecture, implementation, and day-to-day operation of the centralized AI Gateway / Control Tower as the enterprise control point for model access, routing, governance, cost management, and operational oversight
- Own the strategy, architecture, implementation, and day-to-day operation of the centralized AI Gateway / Control Tower as the enterprise control point for model access, routing, governance, cost management, and operational oversight
- Intelligent model routing, provider abstraction, fallback, failover, rate limiting, and workload optimization across approved models
- Intelligent model routing, provider abstraction, fallback, failover, rate limiting, and workload optimization across approved models
- AI FinOps, including token and consumption visibility, budgeting, chargeback/showback, cost allocation, forecasting, and model-cost optimization
- AI FinOps, including token and consumption visibility, budgeting, chargeback/showback, cost allocation, forecasting, and model-cost optimization
- Centralized guardrails for content safety, prompt-injection defense, data-loss prevention, sensitive-data handling, and responsible AI policy enforcement
- Centralized guardrails for content safety, prompt-injection defense, data-loss prevention, sensitive-data handling, and responsible AI policy enforcement
- Identity, access, security, privacy, regulatory compliance, and lifecycle governance controls for models, agents, tools, and AI interactions
- Identity, access, security, privacy, regulatory compliance, and lifecycle governance controls for models, agents, tools, and AI interactions
- End-to-end observability, telemetry, quality monitoring, latency and reliability metrics, incident response, and operational health management
- End-to-end observability, telemetry, quality monitoring, latency and reliability metrics, incident response, and operational health management
- Usage analytics, immutable audit trails, policy evidence, risk reporting, and executive-level transparency across the enterprise AI estate
- Usage analytics, immutable audit trails, policy evidence, risk reporting, and executive-level transparency across the enterprise AI estate
- Model onboarding, approval, versioning, deprecation, resiliency, capacity management, and third-party provider governance
- Model onboarding, approval, versioning, deprecation, resiliency, capacity management, and third-party provider governance
- Retrieval and knowledge systems: RAG pipelines, vector search, document retrieval, and the grounding patterns that make enterprise content usable by agents
- Retrieval and knowledge systems: RAG pipelines, vector search, document retrieval, and the grounding patterns that make enterprise content usable by agents
- Agent lifecycle and quality: deployment, versioning, evaluation frameworks, reliability measurement, and continuous improvement of agents in production
- Agent lifecycle and quality: deployment, versioning, evaluation frameworks, reliability measurement, and continuous improvement of agents in production
- Standards and technical leadership: platform APIs, service contracts, architecture patterns, and the engineering practices other teams build against
- Standards and technical leadership: platform APIs, service contracts, architecture patterns, and the engineering practices other teams build against
- Define the technical vision, architecture, and roadmap for Vertex’s enterprise agentic AI platform
- Define the technical vision, architecture, and roadmap for Vertex’s enterprise agentic AI platform
- Design and build reusable platform services that accelerate development of safe, reliable, and scalable AI-powered applications
- Design and build reusable platform services that accelerate development of safe, reliable, and scalable AI-powered applications
- Lead architecture and implementation for RAG pipelines, knowledge retrieval systems, prompt workflows, tool use, and agent orchestration
- Lead architecture and implementation for RAG pipelines, knowledge retrieval systems, prompt workflows, tool use, and agent orchestration
- Establish core frameworks for agent lifecycle management, including deployment, monitoring, observability, evaluation, and continuous improvement
- Establish core frameworks for agent lifecycle management, including deployment, monitoring, observability, evaluation, and continuous improvement
- Develop scalable infrastructure patterns for enterprise AI workloads, including model integration, data access, and orchestration services
- Develop scalable infrastructure patterns for enterprise AI workloads, including model integration, data access, and orchestration services
- Partner closely with product, engineering, data, security, and UX teams to deliver common AI platform capabilities that support multiple use cases across Vertex
- Partner closely with product, engineering, data, security, and UX teams to deliver common AI platform capabilities that support multiple use cases across Vertex
- Establish best practices for AI reliability, evaluation, safety, and performance measurement
- Establish best practices for AI reliability, evaluation, safety, and performance measurement
- Drive technical standards for platform APIs, service contracts, architecture patterns, and reusable components
- Drive technical standards for platform APIs, service contracts, architecture patterns, and reusable components
- Evaluate emerging technologies, frameworks, and vendors in the AI/agentic ecosystem and make strategic recommendations
- Evaluate emerging technologies, frameworks, and vendors in the AI/agentic ecosystem and make strategic recommendations
- Mentor engineers and influence cross-functional technical teams through architectural leadership and hands-on guidance
- Mentor engineers and influence cross-functional technical teams through architectural leadership and hands-on guidance
- Ensure platform solutions align with enterprise requirements for scalability, resilience, security, and maintainability
- Ensure platform solutions align with enterprise requirements for scalability, resilience, security, and maintainability
- Contribute to Vertex’s long-term AI strategy by identifying opportunities to expand platform capabilities and increase enterprise adoption
- Contribute to Vertex’s long-term AI strategy by identifying opportunities to expand platform capabilities and increase enterprise adoption
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