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About This Role
Our world is transforming, and PTC is leading the way. Our software brings the physical and digital worlds together, enabling companies to improve operations, create better products, and empower people in all aspects of their business.
Our people make all the difference in our success. Today, we are a global team of nearly 7,000 and our main objective is to create opportunities for our team members to explore, learn, and grow – all while seeing their ideas come to life and celebrating the differences that make us who we are and the work we do possible.
Senior Principal AI Solutions Architect
Professional Services
Role Overview
PTC is seeking an experienced, customer-facing Senior Principal AI Solutions Architect to lead the successful positioning, design, validation, and delivery of enterprise AI solutions across our America’s customer base.
This role serves as the critical bridge between Sales, Solution Consulting, Customer Success, and Professional Services. The individual will partner directly with customers during the sales cycle to define business outcomes, validate AI use cases, assess technical feasibility, and architect scalable solutions that align customer requirements with PTC technology and infrastructure capabilities.
As a trusted technical advisor, this individual will help customers bring AI from concept to production by ensuring that proposed solutions are technically sound, operationally viable, and capable of delivering measurable business value. Following project approval, this individual will remain engaged throughout implementation, guiding Professional Services teams to ensure successful execution and adoption.
In addition, this role will act as PTC Professional Services' AI subject matter expert, developing services offerings, best practices, AI enhanced delivery methodologies and tooling, reusable assets, and enablement programs that elevate the capabilities of our Solution Architects, Consultants, Business Solutions Managers (BSMs), and Project/Program Managers.
This position reports directly to the Vice President of Americas Professional Services.
Key Responsibilities
AI Solution Strategy & Pre-Sales Leadership
- Partner with Sales, Solution Consulting, Product Management, and Customer Success teams on strategic AI opportunities.
- Engage directly with executive and technical customer stakeholders to understand business objectives, operational challenges, and desired outcomes.
- Lead AI discovery workshops and facilitate business process analysis.
- Define AI-focused use cases, user stories, business requirements, and success criteria.
- Validate technical feasibility, operational readiness, and implementation complexity.
- Identify risks, dependencies, integration challenges, and infrastructure requirements early in the sales cycle.
- Support solution scoping, estimates, Statements of Work (SOWs), and implementation roadmaps.
- Act as a trusted advisor to customers evaluating enterprise AI transformation initiatives.
Solution Architecture & Technical Leadership
- Configure and lead on end-to-end AI solution architectures across customer environments and PTC platforms.
- Create and present: + Marketecture diagrams + Solution architecture diagrams + Integration architectures + Data flow diagrams + Deployment models + Operational support models
- Ensure integrations, data quality, governance, security, scalability, and operational requirements are fully validated.
- Translate business requirements into actionable technical architectures and implementation plans.
- Conduct architecture reviews and provide technical governance throughout the engagement lifecycle.
Delivery Execution & Customer Success
- Serve as the architectural lead during implementation projects.
- Mentor and guide delivery teams to ensure designs are executed as intended.
- Participate in project governance and steering committee reviews.
- Resolve architectural roadblocks and complex technical challenges.
- Ensure implementations adhere to best practices and achieve stated customer business outcomes.
- Provide expert guidance on enterprise deployment patterns, integration strategies, and change management considerations.
Practice Development & Enablement
- Propose and advise on AI delivery standards, frameworks, and repeatable methodologies for Professional Services.
- Propose and advise on reusable accelerators, templates, reference architectures, and implementation assets.
- Propose and advise on AI-centered service offerings and implementation approaches.
- Identify and drive enhanced-by-AI Professional Services delivery methodologies and tooling
- Lead internal enablement programs and workshops.
- Coach and mentor: + Solution Architects + Implementation Consultants + Business Solutions Managers + Project Managers + Program Managers
- Drive continuous improvement and knowledge sharing across the global Professional Services organization.
Industry Expertise & Innovation
- Stay current with rapidly evolving AI technologies, platforms, frameworks, and industry trends.
- Evaluate emerging technologies and determine applicability to customer use cases.
- Help shape PTC's AI services strategy and delivery capabilities.
Serve as a visible thought leader for AI within Professional Services and across customer engagements. *
Required Qualifications
Experience
- 10+ years of experience in enterprise software consulting, solution architecture, professional services, or systems engineering.
- 5+ years leading complex enterprise transformations involving cloud, data, analytics, automation, or AI technologies.
- Hands-on experience with LLMs, multi-agent systems, prompt engineering, and vector databases (LangChain/LangGraph required).
- Demonstrated experience bringing AI solutions from concept through production deployment.
- Proven experience supporting both pre-sales pursuits and implementation delivery.
- Experience working with senior-level customer stakeholders.
Strong background in large-scale enterprise deployments and digital transformation programs. *
Technical Expertise
- Experience designing and implementing AI, machine learning, generative AI, and intelligent automation solutions.
- Proficiency in Python, SQL, and at least one additional language (Jave, Go, or TypeScript)
- Experience in large-scale data technologies (e.g., Spark, Kafka) and data architecture fundamentals.
- Hands-on experience with LLMs, multi-agent systems, prompt engineering, and vector databases (LangChain/LangGraph required).
- Strong data science capabilities to extract insights and understand client data deeply.
- Strong understanding of: + Enterprise architecture + Data architecture + API integration patterns + Cloud platforms + Security and governance frameworks + Infrastructure design
- Ability to assess technical feasibility and operational readiness for enterprise AI initiatives.
Experience producing and presenting architectural and technical solution documentation. *
Industry Experience
Strong preference for candidates with experience in one or more of the following:
- Manufacturing
- Industrial Products
- Automotive
- Aerospace & Defense
- Medical Device
- High Tech
Field Service Operations *
PTC Domain Experience (Highly Preferred)
Experience with one or more of the following:
- CAD
- PLM
- SLM
- ALM
- Digital Thread initiatives
- Engineering and Product Development processes
Service Lifecycle Management *
Leadership Competencies
The ideal candidate:
- Is passionate about AI and actively explores emerging AI capabilities both professionally and personally.
- Is highly self-motivated.
- Possesses excellent executive communication and presentation skills.
- Can communicate effectively with both business and technical audiences.
- Demonstrates strong consultative problem-solving abilities.
- Builds trust and credibility with customers and internal stakeholders.
- Is collaborative and team oriented.
- Has a bias for action and execution.
- Leads through influence rather than authority.
Operates comfortably in ambiguous, evolving environments. *
Success Metrics
- AI services bookings influenced
- AI implementation success rate
- Customer satisfaction (CSAT)
- Time-to-value achievement
- AI delivery methodology adoption
- Reusable asset creation and utilization
- Internal enablement effectiveness
Delivery quality and project outcome attainment *
PTC carefully considers a wide variety of factors when determining compensation. The anticipated annual salary range for this position is between $149,000-$223,000. The salary range reflects a good-faith estimate of compensation at the time of posting. Actual compensation may vary based on a candidate's skills, qualifications, experience, and location. Additionally, candidates may be eligible for a performance-based bonus. Employees also have the opportunity to become a PTC shareholder through our employee share purchase program (ESPP), which allows for the purchase of discounted PTC stock. Certain roles may also be eligible for participation in our equity programs. Employees may be eligible for medical, dental and vision insurance, paid time off and sick leave, tuition reimbursement, 401(k) contributions and employer match, flexible spending accounts, life insurance, disability coverage and, if you are an office-assigned employee, a generous commuter subsidy. All total rewards and benefits programs are subject to applicable plan eligibility and other terms and conditions.
For more information about PTC’s comprehensive benefits, please visit our Careers Page .
PTC recruits on a rolling basis. Candidates are considered as they apply, until the opportunity is filled.
This position is not eligible for employment-based immigration sponsorship. Applicants must be authorized to work in the United States without requiring current or future sponsorship from PTC.
At PTC, we believe in the power of diverse ideas and perspectives. As a global company that values and respects all identities, cultures, and perspectives, we strive to create an inclusive PTC for ALL through an environment where everyone feels like they belong and are empowered to bring their true, authentic selves to work. Proud to be an Equal Opportunity Employer, we welcome applicants from all backgrounds and hire without regard to race, national origin, religion, age, color, ethnicity, ancestry, marital status, sex (including pregnancy), sexual orientation, gender identity, gender expression, genetic information, disability, veteran status, or any other characteristic protected by local, state, or federal laws, rules, or regulations. It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.
For more information about PTC’s comprehensive benefits and our AI usage, please visit our Careers Page (https://www.ptc.com/en/careers/united-states-careers). Applications will be accepted on an on-going basis until the opportunity is filled.
PTC endeavors to make ptc.com/careers accessible to all users. If you would like to contact us regarding the accessibility of our website or need assistance completing the application process, please contact PTC's Talent Acquisition team at [email protected]. This contact information is for accommodation requests only and cannot be used to inquire about the status of applications.
Life at PTC is about more than working with today’s most cutting-edge technologies to transform the physical world. It’s about showing up as you are and working alongside some of today’s most talented industry leaders to transform the world around you.
If you share our passion for problem-solving through innovation, you’ll likely become just as passionate about the PTC experience as we are. Are you ready to explore your next career move with us?
We respect the privacy rights of individuals and are committed to handling Personal Information responsibly and in accordance with all applicable privacy and data protection laws. Review our Privacy Policy here ."
Salary Context
This $149K-$223K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At PTC, 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 $212,040 based on 13,780 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($186K) sits 12% below the category median. Disclosed range: $149K to $223K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
PTC AI Hiring
PTC has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Remote, US, Boston, MA. Compensation range: $223K - $223K.
Remote Work Context
Remote AI roles pay a median of $188,200 across 2,430 positions. About 6% of all AI roles offer remote work.
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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 median, while Prompt Engineer roles sit at $140,000. 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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