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Job Description
ABOUT VICTAULIC
Victaulic, headquartered in Easton, Pennsylvania, is the global leader in mechanical pipe joining solutions, delivering advanced technologies and engineering services to address complex piping challenges faced by engineers, site owners and contractors. Since 1919, our innovative solutions have improved construction productivity and reduced risks, ensuring projects are completed safely, on time, and within budget. With over 6,000 employees spanning 50+ locations worldwide, our teams are dedicated to delivering advanced technologies and engineering expertise that address real-world challenges across a wide range of industries.
Our sustainable solutions, made from high quality, graded scrap steel, promote circular construction by making piping systems infinitely reconfigured and minimizing environmental impact. Today, our high-quality products are used in more than 140 countries, supporting some of the world's most critical and prestigious structures.
An Industry Leader With a Heart as Big as Its Ambition and Innovation
At Victaulic, we're committed to constructing a better world for and with our people.
Let's Build a Better World Together.
Our commitment to our people is built on four pillars:
- We build from within.
- We stand behind our people.
- We foster an environment for constant learning.
- We engineer change for a more stable and sustainable world.
YOUR IMPACT STARTS HERE
Victaulic's Enterprise Data & AI Services organization is scaling a formal AI Governance capability spanning policy, risk classification, vendor and model risk assessment, and platform-enabled compliance monitoring. As Senior AI Risk Analyst, you will execute and continuously improve Victaulic's AI risk assessment processes by evaluating AI use cases, vendor and model risk, and agentic and generative AI deployments against Victaulic's AI Governance Framework (V-RAIF) and Acceptable Use Policy (AUP v2.0). You will work hands-on in Victaulic's AI governance platform, Credo AI, and partner closely with Legal, Cybersecurity, Procurement, and business stakeholders to help AI adoption move quickly without introducing unmanaged risk.
WHAT YOU'LL BUILD & OWN
- Execute end-to-end AI use case risk assessments using Victaulic's formal 41-item risk assessment checklist across six phases, producing clear risk ratings and mitigation recommendations.
- Administer and continuously improve AI risk workflows in Credo AI, including intake forms, risk-scoring logic, evidence collection, and reporting dashboards.
- Apply Victaulic's three-tier risk classification model to new AI agents, generative AI tools, and MCP or connector integrations as they move through the registration process.
- Lead AI vendor and contract risk reviews, including data rights, intellectual property ownership, audit rights, and model change notification, in partnership with Legal and Procurement.
- Build and maintain repeatable governance processes and playbooks for intake, triage, escalation, remediation tracking, and periodic reassessment so risk review scales with AI adoption across the business.
- Prepare risk findings, metrics, and talking points for the AI Governance Committee and executive updates.
- Monitor the regulatory and standards landscape, including NIST AI RMF, ISO/IEC 42001, and the EU AI Act, and translate relevant changes into updates to Victaulic's Acceptable Use Policy and governance artifacts.
- Partner with AI Engineering, Data Governance, and AI Enablement & Adoption to ensure risk controls are embedded in delivery rather than added after the fact.
- Support insurance and cyber-risk assessments related to AI use and contribute to incident and near-miss reviews involving AI systems.
WHAT YOU BRING
- 5+ years of experience in AI or technology risk, compliance, information security, or IT governance, including at least 2 years focused specifically on AI or machine learning risk.
- Direct, hands-on experience configuring workflows, risk assessments, or governance reporting in Credo AI.
- A demonstrated track record of building AI governance processes from the ground up, such as intake and triage workflows, risk classification models, or assessment checklists.
- Working knowledge of AI risk and governance frameworks, such as NIST AI RMF, ISO/IEC 42001, or an equivalent framework, and how they translate into operational controls.
- Experience evaluating third-party AI or SaaS vendor contracts for data rights, intellectual property, and audit-rights risk.
- Strong written and verbal communication skills, including experience preparing materials for governance committees or senior stakeholders.
- Structured, checklist-driven risk thinking balanced with pragmatic judgment about business impact.
- Comfort operating with ambiguity while a governance program is being built.
- Strong cross-functional collaboration skills and the ability to work fluently with Legal, Cybersecurity, Procurement, and business and technical teams.
- A platform-oriented mindset that treats Credo AI as a system to configure and improve, not simply a form to complete.
- Bachelor's degree in Information Systems, Risk Management, Computer Science, a related field, or equivalent practical experience.
WHAT SETS YOU APART
- Familiarity with agentic AI and Model Context Protocol (MCP) integration risk.
- Certification such as CIPP/AI, AIGP, CISA, or CRISC.
- Experience in a manufacturing or industrial enterprise IT environment.
WHAT WE OFFER
Build A Better Career
- Competitive compensation with performance-based incentives.
- Career development programs and internal promotion pathways - we build from within.
- Tuition reimbursement and continuous learning opportunities.
Build A Better Future
- Comprehensive health, dental, and vision coverage.
- Retirement savings plans with company match.
- Life insurance and disability coverage.
Build A Better You
- Paid time off, holidays, and flexible work arrangements where applicable.
- Employee Assistance Programs (EAP) and wellness resources.
- Inclusive, team-oriented culture where people look out for one another.
Build A Better World
- Join a company whose products contribute to a safer, more sustainable built environment every day.
IMMIGRATION SPONSORSHIP
Victaulic does not provide immigration sponsorship (e.g., H-1B, TN, or other work visa sponsorship) for this position. All applicants must be currently authorized to work in the applicable country on a full-time basis without the need for employer-sponsored immigration support now or in the future.
EQUAL EMPLOYMENT OPPORTUNITY
Victaulic is an Equal Employment Opportunity (EOE/M/F/Vets/Disabled) employer and welcomes all qualified applicants. Applicants will receive fair and impartial consideration without regard to race, gender, color, religion, national origin, age, disability, veteran status, sexual orientation, genetic data, or other legally protected status. Background checks may be required as part of our pre-employment process.
VICTAULIC STAFFING PARTNER COMMUNICATION POLICY
All staffing agencies are strictly forbidden from directly contacting any Victaulic employees, except those within the Human Resources/Talent Acquisition team. All communications, inquiries, and candidate submissions must be routed through Victaulic's Human Resources/Talent Acquisition team. Non-compliance with this policy may result in the suspension of partnership, cancellation of the current contract, and/or the imposition of a mandatory probation period before any future business can resume. Additionally, non-compliance may lead to a permanent ban on future business. This policy ensures a streamlined and compliant recruitment process.
Let's Build a Better World Together.
victaulic.com/careers
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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Victaulic, 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 in Demand for This Role
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 $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Victaulic AI Hiring
Victaulic has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in PA, US.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 median).
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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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