AI Engineer

Overview

Are you someone who is passionate, motivated, and driven to make a difference? If so, MSA Safety is the perfect fit for your career.


At MSA, SAFETY is who we are AND it is what we do. We are a purpose-driven company committed to deploying innovation and technology to deliver on our Mission to help protect people and assets all around the world. We continue to be relentless in our pursuit of solving our customers greatest problems so they can go home safe each and every day.


Are you in? Read on for more details about this particular role.


Responsibilities

About the Role:

We're building an AI Platform that empowers users across business functions to connect with enterprise data and accomplish everything from intelligent search to agentic workflows for task automation and artifact generation. The platform is accelerating business processes and shaping new ways we serve and innovate for our customers, with growing use cases across frontline support, NPD engineering, and business development.

As an AI Engineer on this team, you'll help design, build, and scale the systems behind this work. You'll work across the stack: orchestrating LLMs and agents, integrating with enterprise data sources, deploying in the cloud, and iterating based on how real users actually use what we build.

We move fast, and we're looking for someone who thrives in that environment: adaptable, quick to learn, and an excellent communicator who can turn ambiguity into clear direction and shipped work.

What You'll Do:

• Design and build AI-enabled applications and services powering intelligent search, agentic workflows, and artifact generation
• Develop and orchestrate agentic systems, including tool use, multi-step reasoning, and evaluation loops grounded in real-world feedback
• Deploy, monitor, and continuously improve models and AI services on AWS
• Partner with stakeholders across MSA business functions to translate real needs into tools that drive productive use
• Contribute to architectural discussions and help drive technical decisions across the team
• Write clean, maintainable, well-documented code and uphold strong engineering practices (Git, code review, CI/CD, testing)

Qualifications

This position is posted at multiple levels:

• Level I: 0-1 years of experience
• Level II: 1-3 years of experience
• Senior: 3-5 years of experience

Required Education and Experience:

• Bachelor's Degree in AI Engineering, Data Science, Computer Science, Statistics, Mathematics, Engineering or related quantitative discipline
• Experience building AI-enabled applications, ideally involving agentic workflows, tool orchestration, and evaluation
• Experience with MLOps practices, including model deployment, monitoring, and evaluation in a cloud environment (AWS preferred)
• Proficiency in Python; TypeScript is a plus
• Working experience with at least one major ML framework (PyTorch or TensorFlow)
• Solid understanding of transformer-based models, including multimodal architectures, and familiarity with parameter-efficient fine-tuning (e.g., LoRA, QLoRA) and model optimization techniques (quantization, pruning, distillation)
• Understanding of how software works across the stack and in the cloud, including containers, serverless functions, databases, and observability/telemetry
• Strong software engineering fundamentals and a track record of clean, well-tested code
• Adaptability and a fast pace, with the curiosity to pick up new tools and frameworks as the AI landscape evolves
• Excellent communication skills, with the ability to articulate complex technical concepts to both technical and non-technical stakeholders

Preferred Education and Experience:

• Master's Degree
• Experience working with open-source models via Hugging Face and managed services such as AWS Bedrock and Bedrock AgentCore, as well as agent SDKs like Strands
• Experience working with enterprise ERP and CRM systems such as SAP and Salesforce
• Experience in an agile development environment
• Experience with Vision Transformers and/or CNNs
• Familiarity with SQL, ETL, and working with large datasets

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