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About This Role
ASME is seeking a Principal AI \& Knowledge Graph Architect to join our Information Technology team. The Principal AI \& Knowledge Graph Architect will be responsible for leading ASME’s technical transformation of engineering standards into intelligent, machine\-interpretable digital assets. This role combines expertise across AI systems architecture, knowledge engineering, ontology design, graph technologies, and enterprise\-scale digital transformation. It requires balancing innovation with governance ensuring ASME’s retention of authority, traceability, and control over the digital meaning of its intellectual property.
Key responsibilities include:
Knowledge Graph \& Digital Asset Architecture
- Define and own the multi\-year strategy and roadmap for ASME’s Digital Engineering Knowledge Platform.
- Drive the integration of ASME standards intelligence into engineering ecosystems, including CAD/CAE, PLM, MBSE, digital twins, and AI\-powered engineering tools.
- Establish a unified digital platform connecting standards, learning, journals, technical content, APIs, and AI\-enabled services.
- Explore and operationalize emerging interoperability protocols and frameworks, including MCP, ACP, semantic interoperability standards, and AI orchestration technologies.
- Prioritize enterprise platform capabilities and drive scalable growth across business units, content domains, and external standards ecosystems.
AI, Semantic Intelligence \& Engineering Automation
- Define ASME’s domain\-specific AI architecture strategy, including integration of SLMs, LLMs, RAG pipelines, vector databases, and semantic retrieval frameworks.
- Lead architecture for AI\-enabled engineering use cases, including:
+ Automated compliance validation
+ AI\-assisted engineering workflows
+ Intelligent standards retrieval
+ CAD/PLM/MBSE integrations
+ AI agent and machine\-consumable standards
- Define Human\-in\-the\-Loop (HITL) validation frameworks to ensure trust, technical fidelity, and explainability.
- Establish technical guardrails for AI usage, hallucination mitigation, and authoritative engineering interpretation.
- Partner with vendors and research organizations to evaluate emerging AI and graph technologies.
Governance, Strategy \& Enterprise Leadership
- Serve as ASME’s senior technical authority for digital knowledge architecture and AI\-enabled standards transformation.
- Define governance standards for semantic consistency, relationship integrity, validation workflows, and lifecycle management.
- Establish architectural and semantic boundaries for external vendors, partners, and AI providers to protect ASME IP and authoritative meaning.
- Collaborate with SMEs, Standards leadership, Enterprise Architecture, Legal, Product, and Technology teams to align technical transformation with organizational strategy.
- Translate complex AI, semantic, and graph concepts into executive\-level guidance, board\-ready communication, and strategic recommendations.
- Contribute to future\-state digital platform strategy, including monetization, APIs, machine\-accessible delivery, and multi\-SDO interoperability.
Demonstrated knowledge and expertise across the following areas is required:
- Designing enterprise\-scale knowledge graphs, ontologies, semantic models, or AI\-enabled information platforms.
- Ability to lead complex technical transformation initiatives involving structured and unstructured data.
- Experience defining governance and validation frameworks in regulated or technically rigorous domains.
- Strong understanding of interoperability standards, APIs, and machine\-consumable information architectures.
- Ability to operate as a strategic technical leader across executives, SMEs, technology teams, legal stakeholders, and external partners.
- Exceptional communication and executive presentation skills.
Preferred qualifications include:
- 8\+ years of experience in one or more of the following:
- Enterprise Architecture
- AI/ML Systems Architecture
- Knowledge Graphs \& Semantic Technologies
- Data \& Information Architecture
- Digital Platform Engineering
- Experience with engineering, manufacturing, industrial, or standards\-based domains.
- Exposure to CAD, PLM, MBSE, simulation, or digital twin ecosystems.
- Familiarity with graph databases, ontology frameworks, and semantic standards.
- Experience working with AI vendors, research organizations, or emerging technology ecosystems.
This role is eligible for telecommuter work arrangement. Periodic business travel will be required, including but not limited to ASME Offices which are currently located in NYC (headquarters), NJ, DC, TX.
ASME is proud to be an Equal Opportunity Employer. At ASME, we nurture an inclusive environment, and we encourage, support, and celebrate diversity in the workplace. All qualified applicants will receive consideration for employment and will not be discriminated against on the basis of race, color, religion, sex (including pregnancy), national origin, age, gender identity \& expression, sexual orientation, genetic information, citizenship status, disability, or protected veteran, military status, or any other basis protected by law.
Our Equal Employment Opportunity policy pertains to every aspect of an individual’s relationship with the organization, including but not limited to recruitment, hiring, compensation, benefits, training and development, promotion, programs, and all other terms and conditions of employment.
Annual base salary may vary based on geographic location. The New York City Metro salary range for this position is estimated to be between $150,000 \- $180,000 per year.
Only those candidates selected for further consideration will be contacted.
Salary Context
This $150K-$180K range is below the median 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 ASME International, 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 ($165K) sits 23% below the category median. Disclosed range: $150K to $180K.
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.
ASME International AI Hiring
ASME International has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $180K - $180K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 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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