Senior Principal AI Engineer
Vertex Pharmaceuticals
Senior Principal AI Engineer
Job Description
Job DescriptionVertex 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 #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
- software engineeringintermediate
- seniorintermediate
- distributed systemsintermediate
- enterprise AI platformsintermediate
- frameworksintermediate
- model gatewaysintermediate
- vector databasesintermediate
- regulated industries such as biotechnologyintermediate
- highly collaborative environmentsintermediate
Required Qualifications
- Bachelor’s degree in Computer Science, Software Engineering, Machine Learning, Data Engineering, or a related technical field; advanced degree preferred (degree in computer science)
- Bachelor’s degree in Computer Science, Software Engineering, Machine Learning, Data Engineering, or a related technical field; advanced degree preferred (degree in computer science)
- Significant industry experience in software engineering, machine learning engineering, or AI platform development, including experience in senior or principal-level technical roles (experience)
- Significant industry experience in software engineering, machine learning engineering, or AI platform development, including experience in senior or principal-level technical roles (experience)
- Proven track record designing and delivering production-scale AI or ML platforms (experience)
- Proven track record designing and delivering production-scale AI or ML platforms (experience)
- Strong experience building distributed systems, APIs, microservices, and cloud-native applications (experience)
- Strong experience building distributed systems, APIs, microservices, and cloud-native applications (experience)
- Demonstrated experience operationalizing machine learning, generative AI, or agent-based solutions in enterprise environments (experience)
- Demonstrated experience operationalizing machine learning, generative AI, or agent-based solutions in enterprise environments (experience)
- Experience designing extensible platform capabilities that enable internal teams to build or integrate custom applications, tools, or services (experience)
- Experience designing extensible platform capabilities that enable internal teams to build or integrate custom applications, tools, or services (experience)
- Deep understanding of software engineering best practices including testing, CI/CD, version control, code review, and system reliability (experience)
- Deep understanding of software engineering best practices including testing, CI/CD, version control, code review, and system reliability (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)
- Experience leading complex technical initiatives and influencing architecture across cross-functional teams (experience)
- Experience leading complex technical initiatives and influencing architecture across cross-functional teams (experience)
- Strong communication skills with the ability to explain complex technical concepts to varied audiences (experience)
- Strong communication skills with the ability to explain complex technical concepts to varied audiences (experience)
- Experience balancing experimentation speed with production engineering discipline, security, and maintainability (experience)
- Experience balancing experimentation speed with production engineering discipline, security, and maintainability (experience)
- AI/ML platform architecture (experience)
- AI/ML platform architecture (experience)
- Generative AI systems and large language model integration (experience)
- Generative AI systems and large language model integration (experience)
- Agentic workflows and orchestration frameworks (experience)
- Agentic workflows and orchestration frameworks (experience)
- Multi-agent or autonomous agent system design (experience)
- Multi-agent or autonomous agent system design (experience)
- Prompt engineering and prompt management (experience)
- Prompt engineering and prompt management (experience)
- Workflow orchestration and automation (experience)
- Workflow orchestration and automation (experience)
- Agent lifecycle management (experience)
- Agent lifecycle management (experience)
- Model evaluation, benchmarking, and performance measurement (experience)
- Model evaluation, benchmarking, and performance measurement (experience)
- AI observability, tracing, monitoring, and logging (experience)
- AI observability, tracing, monitoring, and logging (experience)
- API design and service integration (experience)
- API design and service integration (experience)
- Distributed systems and scalable backend engineering (experience)
- Distributed systems and scalable backend engineering (experience)
- Cloud platforms and cloud-native deployment patterns (experience)
- Cloud platforms and cloud-native deployment patterns (experience)
- Productionization of AI/ML services (experience)
- Productionization of AI/ML services (experience)
- Reliability, latency, throughput, and cost optimization (experience)
- Reliability, latency, throughput, and cost optimization (experience)
- CI/CD pipelines and DevOps/MLOps practices (experience)
- CI/CD pipelines and DevOps/MLOps practices (experience)
- Secure software development and enterprise platform controls (experience)
- Secure software development and enterprise platform controls (experience)
Preferred Qualifications
- Advanced degree in Computer Science, Artificial Intelligence, Machine Learning, or a related discipline (degree in computer science)
- Advanced degree in Computer Science, Artificial Intelligence, Machine Learning, or a related discipline (degree in computer science)
- Experience with enterprise AI platforms, developer platforms, or internal tooling ecosystems (experience)
- Experience with enterprise AI platforms, developer platforms, or internal tooling ecosystems (experience)
- Experience building frameworks or platforms that support bring-your-own-component or extensible developer patterns (experience)
- Experience building frameworks or platforms that support bring-your-own-component or extensible developer patterns (experience)
- 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 (experience)
- 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 (experience)
- Familiarity with model gateways, retrieval-augmented generation, and evaluation frameworks (experience)
- Familiarity with model gateways, retrieval-augmented generation, and evaluation frameworks (experience)
- Experience implementing AI governance, responsible AI controls, and compliance-oriented technical solutions (experience)
- Experience implementing AI governance, responsible AI controls, and compliance-oriented technical solutions (experience)
- Knowledge of vector databases, knowledge retrieval systems, and orchestration layers for intelligent applications (experience)
- Knowledge of vector databases, knowledge retrieval systems, and orchestration layers for intelligent applications (experience)
- Experience in regulated industries such as biotechnology, pharmaceuticals, healthcare, or life sciences (experience)
- Experience in regulated industries such as biotechnology, pharmaceuticals, healthcare, or life sciences (experience)
- Strong mentoring and technical leadership experience in highly collaborative environments (experience)
- Strong mentoring and technical leadership experience in highly collaborative environments (experience)
- Ability to assess emerging AI technologies and translate them into practical platform capabilities (experience)
- Ability to assess emerging AI technologies and translate them into practical platform capabilities (experience)
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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Drive platform adoption by creating reusable components, documentation, onboarding patterns, and developer enablement resources
- Mentor engineers and provide technical leadership across AI platform initiatives
- 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
- Evaluate emerging tools, frameworks, and architectural patterns in generative AI and agentic systems to inform platform strategy
Tags & Categories
Answer 10 quick questions to check your fit for Senior Principal AI Engineer @ Vertex Pharmaceuticals.
10 Questions
~2 Minutes
Instant Score