Interested in this AI Architect role at MSC Industrial Supply Co.?
Apply Now →Skills & Technologies
About This Role
BUILD A BETTER CAREER WITH MSC
Serving customers and our community starts with the best people doing their best work. That is precisely what we have created at MSC Industrial Supply Co. We are a leading North American distributor of metalworking and maintenance, repair, and operations (MRO), products and services. We offer more than 2 million products and have over 80 years of experience across dozens of industries. We aim to execute our Built to Make You Better brand promise for all our stakeholders, including our associates.
Requisition ID :21691
Employment Type : Full Time
Job Category : Digital/IT
Work Location : Melville, NY (CSC)
BRIEF POSITION SUMMARY:
The Principal AI Architect serves as the principal technical design authority within the Enterprise Data Science & AI Center of Excellence (CoE). This role is responsible for architecting enterprise-grade AI and agentic reference frameworks, defining the core technical foundations that support them, and leading the design and implementation of the company's knowledge and context infrastructure. The role serves as a critical cross-functional bridge — partnering with architects across Information Security, Cloud Infrastructure, Data Platform, and Application Development to ensure enterprise-wide technical alignment. The Principal AI Architect also guides technical delivery, working closely with data scientists, MLOps specialists, and AI developers to translate complex AI models into scalable, production-grade enterprise software.
DUTIES AND RESPONSIBILITIES
- Technical Strategy & Roadmap : Own the technical roadmap for AI and agentic systems architecture, establishing the standards, patterns, and technical direction that guide AI solution design across the enterprise.
- Final Architectural Authority : Own final decision-making authority on AI and agentic system architecture, resolving cross-team technical conflicts and ensuring consistency across all AI initiatives.
- Reference Architecture & Standards:Define, maintain, and enforce enterprise-wide AI reference architectures and technical quality evaluation baselines to ensure software reliability across all product teams.
- Sovereign Stack Design:Architect and maintain the secure, internal sovereign AI layered stack natively embedded inside enterprise Google Cloud Platform (GCP) and Azure tenants.
- Cross-Architecture Partnership:Partner extensively with enterprise architects acrossInformation Security, Cloud Infrastructure, Data Platform, and Application Developmentto ensure AI environments are seamlessly integrated, securely isolated, cost-optimized, and compliant with broader infrastructure standards.
- Execution Team Collaboration: Set technical direction for data scientists, MLOps specialists, and AI developers by defining pipeline architecture, model deployment standards, and CI/CD quality gates. Conduct code reviews on key architectural components and integration points to ensure execution stays aligned with the broader AI system design.
- Semantic Context Layer Engineering:Design and build high-fidelity unified metadata environments, semantic graphs, vector pipelines, and enterprise Knowledge Graphs to fuel reliable multi-step agentic reasoning across functional boundaries.
- Model-Agnostic Infrastructure Stack:Establish modular tech stack pathways that decouple application logic from foundational models, enabling engineering teams to seamlessly evaluate, swap, or cascade underlying large language models (LLMs) and small language models (SLMs) without vendor lock-in.
- Modern Composable Core Integration:Collaborate with IT matrix teams to transition legacy technical debt into modern, API-driven pathways and wrappers optimized for agentic tool calling and autonomous execution workflows.
- Tech-Enabled Control Planes:Coordinate with Information Security architects to embed robust, programmatic Responsible AI guardrails directly into system designs, including data loss prevention (DLP), automated code kill-switches, and adversarial filtering mechanisms.
- Product Engineering Partnership:Partner explicitly with the AI Technical Product Manager to evaluate functional capabilities, provide technical scalability inputs, and map probabilistic reasoning logic onto reliable infrastructural components.
- Drive Continuous Improvement:Stay ahead of macroeconomic AI trends, frontier model developments, and emerging cloud platform capabilities to drive continuous improvement and maintain an ongoing structural competitive edge.
- Fosters the MSC Culture in the department and throughout the company to ensure fulfillment of MSC's vision and unity of purpose
- Participation in special projects and performs additional duties as required
QUALIFICATIONS
What You Need:
- A bachelor's degree in Computer Science, Artificial Intelligence, Software Engineering, or a highly technical related engineering field is required; master's degree preferred
- Minimum of 5 years of technical leadership experience architecting AI/ML systems, setting technical standards for AI development teams, and guiding infrastructure and platform decisions to support model training and deployment.
- Minimum of 10 years of progressive experience in software engineering, enterprise architecture, or machine learning/AI infrastructure — including experience designing and deploying production-grade AI systems at scale.
- Extensive, hands-on experience designing systems within top-tier cloud providers (specifically GCP and Azure), including big data platforms (e.g., Databricks, Delta Lake) and ML platforms (e.g., Vertex AI, Azure ML).
- Proven track record shipping complex machine learning solutions, semantic search systems, multi-agent orchestration, or Retrieval-Augmented Generation (RAG) architectures into production.
- Direct experience architecting agentic AI systems — including agent orchestration, autonomous tool-calling, and human-in-the-loop safety controls.
- Demonstrated experience setting AI/ML technical direction and architecture standards adopted across multiple teams and business units.
Bonus Points If You Have:
- Strong expertise in cloud orchestration, API design, and microservices architecture, with working knowledge of CI/CD practices and tooling (e.g., Azure DevOps, Git).
- Hands-on mastery of advanced AI/ML orchestration tools and frameworks, including LangChain, LangGraph, Hugging Face, Ollama, Scikit-Learn, Python ecosystem libraries, and container environments (Docker, Kubernetes).
- Strong understanding of generative AI fundamentals, including large language models, prompt engineering, transformer architectures, vector embeddings, and vector database design.
- Practical understanding of common ML/AI use cases (e.g., recommendation systems, forecasting, churn prediction) sufficient to architect scalable, production-grade infrastructure for them.
- Expert familiarity with enterprise architecture reference frameworks and their application to non-deterministic, probabilistic AI ecosystems.
- Excellent communication skills, with the ability to present complex architectural decisions clearly to both executive stakeholders and hands-on technical teams.
- Self-directed and comfortable operating with a high degree of autonomy, architecting complex, multi-tenant systems with minimal day-to-day oversight.
Other Requirements:
- Position requires up to 10% of travel
- This position may require access to International Traffic in Arms Regulations Information (“ITAR”) and/or Controlled Unclassified Information (“CUI”)
- INDICATES ESSENTIAL DUTIES
To perform this job successfully an associate must be able to perform each essential duty satisfactorily. The requirements listed below are representative of knowledge, experience level and abilities required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential duties.
Compensation starting at $138,500 - $216,559 depending on candidate location and experience.
The salary range represented is based on similar roles in comparable industries, and the cost of labor in respective cities. Actual compensation is based on the candidate's relevant experience, education requirements and peer pay equity. The Company reserves the right to modify the range as market conditions change.
Applicants must be currently authorized to work in the United States. We are unable to sponsor or take over sponsorship of an employment Visa for this position at this time.
This job posting will remain open for a minimum of 3 business days from the original posting date or longer as needed to fill the position.
WHY MSC?
People. Collaboration. Insight. That's how you build something that works. Built on a foundation of trust, MSC works side by side with our customers to help them drive business results. With more than one million product offerings and 80+ years of experience across industries, MSC strives to help our customers achieve greater productivity, profitability, and growth through inventory management and other innovative supply chain solutions. We care about our associates and have programs in place to help our 6,500+ team members achieve their potential.
OUR COMMITMENT TO YOU
Our associates are our top priority and investing in their well-being is one way we execute our Built to Make You Better brand promise. Alongside competitive pay, we have a comprehensive benefits program to support you and your family's health, well-being and financial future. We offer dynamic healthcare plans, generous 401K and stock purchasing programs, tuition reimbursement opportunities, and paid time away for holidays, vacations, and illness. Visit our page for a better look at our extensive benefits: Your Future Benefits.
You will also have the opportunity to join our Associate Inclusion Circles: Women, Pride, Black, Generational, Veterans, HOLA, and Able. These circles are open to all associates and are designed to promote awareness, collaboration, and respect.
EQUAL EMPLOYMENT OPPORTUNITY STATEMENT
At MSC, we are committed to providing an environment of mutual respect where equal employment opportunities are available to all qualified applicants and our associates without regard to race, color, religion, age, sex, national origin, disability, protected veteran status, sexual orientation or any category protected by applicable law. Accommodation requests can be made at any stage of the recruitment process; applicants are asked to make their needs/requirements known.
Please note that MSC uses electronic monitoring and system logs in its workplace, including monitoring of Company-provided systems and collaboration tools, as permitted by applicable law.
Salary Context
This $138K-$216K range is above the median for AI Architect roles in our dataset (median: $175K across 37 roles with salary data).
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 9,079 AI roles we're tracking, AI Architect positions make up 1% of the market. At MSC Industrial Supply Co., this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Architect roles pay a median of $224,070 based on 236 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($177K) sits 21% below the category median. Disclosed range: $138K to $216K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
MSC Industrial Supply Co. AI Hiring
MSC Industrial Supply Co. has 2 open AI roles right now. They're hiring across AI Architect, AI/ML Engineer. Positions span Melville, NY, US, Davidson, NC, US. Compensation range: $216K - $229K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into AI Architect roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.