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About This Role
Job Description
Position Summary
Vertex is seeking a Sr. Principal AI Engineer, Agentic AI Platform \& Control Tower Ops 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 leader will own the vision for a centralized AI Control Tower / Control Plane / Gateway 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 engineering leader who is excited to shape enterprise AI architecture, standards, and long\-term technical direction.
Key Responsibilities
- 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
- Build and evolve a centralized AI Control Tower / Gateway to provide:
+ Agent monitoring and health visibility
+ Operational observability and telemetry
+ Policy enforcement and guardrails
+ Security, governance, and compliance controls
+ Usage analytics, auditability, and operational reporting
- 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
- Control plane / control tower / gateway architecture
- 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\-HYBRID
Pay Range:
$188,000 \- $282,000
Disclosure 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 Information
Vertex 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 [email protected]
Salary Context
This $188K-$282K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Vertex Pharmaceuticals, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($235K) sits 9% above the category median. Disclosed range: $188K to $282K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Vertex Pharmaceuticals AI Hiring
Vertex Pharmaceuticals has 4 open AI roles right now. They're hiring across AI/ML Engineer. Based in Boston, MA, US. Compensation range: $250K - $282K.
Location Context
AI roles in Boston pay a median of $210,000 across 166 tracked positions.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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