Staff AI Engineer

Easton, Pennsylvania, United StatesOnsiteFull TimePosted 1 day agoVisa sponsorship available

Job Description

For over a century, Victaulic has been the world’s leading innovator in mechanical pipe joining. Our systems are inside the infrastructure that powers data centers, hospitals, stadiums, and high-rises across 140+ countries. Now we’re building an AI practice that will reshape how those systems are designed, manufactured, and delivered.

As our Staff AI Engineer, you will be the technical cornerstone of that transformation. You won’t inherit a mature platform and optimize at the margins. You’ll define how AI gets built at Victaulic from the engineering standards that govern every model, to the MLOps infrastructure that makes deployment repeatable, to the responsible AI controls that ensure our systems earn the trust of the engineers and operators who depend on them. You’ll write production code, architect systems, mentor engineers, and set the quality bar for an AI program that is already shipping products and earning executive investment.

This is a hands-on technical leadership role for someone who wants to build something consequential from the foundation up, not manage from a distance.

# Key Responsibilities

Build Production AI Systems: Write production code daily. Lead the engineering of our most complex AI/ML solutions including a RAG-based enterprise search platform (AWS Bedrock + OpenSearch), an order-capacity optimization system, demand forecasting models, and LLM-powered applications serving multiple business units. This role requires at least 50% hands-on development time.
Establish Engineering Excellence: Define the engineering standards, code review practices, testing strategies, and architecture patterns that set the quality bar for the entire AI program. Publish architecture decision records. Lead design reviews. Create reference implementations and technical guides that become the team’s institutional foundation.
Build MLOps from the Ground Up: We have production AI products but no MLOps infrastructure. You’ll build it: CI/CD pipelines for model training and deployment, a model registry with version tracking and lineage, API management for governing model access, and production monitoring with drift detection and alerting. This is greenfield work. Your architecture becomes the foundation everything scales on.
Own Responsible AI Engineering: Translate governance policies into automated technical controls. Build model card generation pipelines, bias testing suites as CI/CD gates, explainability dashboards (SHAP/LIME), and compliance automation mapped to the NIST AI Risk Management Framework and EU AI Act requirements which take effect for high-risk systems in August 2026.
Be the Force Multiplier: Serve as the primary technical mentor for a growing AI engineering team. Conduct code reviews that teach, not just approve. Lead pair programming sessions on complex problems. Elevate every engineer’s ability to design, build, and deploy production-grade AI systems.
Architect What Comes Next: Design the technical architecture for new AI projects from problem framing through production deployment. Evaluate build-vs-buy trade-offs. Ensure coherence across the AI portfolio. Provide architecture consulting to teams across IT building AI-adjacent solutions. Partner with the AI Product Manager, Data Science, and business stakeholders to translate operational challenges into deployable solutions.

# Technical Requirements

Experience: 10+ years of software engineering, with 3+ years deploying AI/ML systems in production environments not just research or prototyping. You’ve shipped models that real users depend on.
The AI Stack: Expert proficiency in Python and PyTorch or TensorFlow. Experience with RAG architectures, LLM application development, or enterprise search systems is highly valued.
Infrastructure: Proven experience building or operating MLOps pipelines (CI/CD for models, model registries, monitoring). Cloud-native architecture experience with AWS (Bedrock, SageMaker) or Azure. Snowflake experience is a plus.
Production Mindset: Strong software engineering fundamentals system design, API design, testing strategies, version control, code review. You build for reliability and maintainability, not just accuracy.
Responsible AI: Experience with any of: model documentation (model cards), bias/fairness testing, explainability tooling, drift monitoring, or regulatory compliance automation. Published research or governance engineering experience is a strong differentiator.
Education: Bachelor’s in Computer Science, Engineering, or a related quantitative field or equivalent professional experience. Advanced degrees valued but not required.

# The “Victaulic Fit”

Builder, Not Advisor: You want to write code, not just review it. You’d rather build a working prototype than a slide deck. Your authority comes from what you ship, not what you present.
Pragmatic Engineer: You value explainable, reliable AI over cutting-edge hype. You understand that in a manufacturing environment, a model that’s 90% accurate and fully understood beats one that’s 95% accurate and a black box.
Systems Thinker: You don’t just optimize a model you optimize the entire system: data pipelines, deployment infrastructure, monitoring, governance, and the human workflows around them.
Effective Translator: You can explain a transformer architecture to a manufacturing engineer with 30 years of domain expertise and learn how a grooved coupling works in return. The best AI solutions at Victaulic come from people who are genuinely curious about how things are built.
Ownership Mentality: You don’t wait for perfect requirements or a fully staffed team. You see what needs to be built, you build it, and you make it better as you go.

# Technology Environment

# Why Victaulic?

We offer the stability of a century-old global manufacturer with the technical ambition of a company that knows AI will define its next hundred years. You won’t be optimizing ad clicks or engagement metrics you’ll be building the AI systems that change how physical infrastructure gets designed, manufactured, and delivered worldwide.

Your work will directly impact factories, supply chains, and the engineers who build the systems inside the world’s most critical structures. You’ll have the ownership and greenfield opportunity of a startup, the resources and stability of a global leader, and the rare chance to define from the foundation how an entire industrial company builds with AI.

Victaulic is headquartered in the Lehigh Valley, PA with easy access to New York and Philadelphia, excellent schools, and a quality of life that lets you focus on building great things without the trade-offs of a major metro.

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