AI Engineer Co-op: Build Enterprise AI Platforms

MSA Safety; Cranberry Township (Butler County); On-site; USD 30,000 - 50,000; Full time

Job summary

MSA Safety is seeking an AI Engineer to help design, build, and scale AI-enabled applications that enable intelligent search and agentic workflows. You will orchestrate tools, deploy models in the cloud, and iterate with real-world feedback.

The right candidate will have hands-on experience with ML frameworks, MLOps, and cloud infrastructure, plus strong communication skills to translate complex ideas to stakeholders across the organization.

Qualifications

• Working towards a Bachelor's / Master’s Degree in AI/ML or related field.
• Experience building AI-enabled applications with agentic workflows.
• Experience with MLOps including deployment, monitoring in cloud environments (AWS preferred).
• Proficiency in Python; TypeScript a plus.
• Experience with PyTorch or TensorFlow.
• Understanding of transformer models, multimodal architectures, and fine-tuning techniques.
• Awareness of cloud stacks, containers, and CI/CD practices.
• Strong communication to explain complex tech to varied stakeholders.

Responsibilities

• Design and build AI-enabled applications powering intelligent search, agentic workflows, and artifact generation.
• Develop and orchestrate agentic systems with tool use and multi-step reasoning.
• Deploy and monitor AI services on AWS; iterate based on user feedback.
• Collaborate with stakeholders to translate needs into usable tools.
• Contribute to architectural discussions and drive technical decisions.
• Write clean, tested code with good documentation and CI/CD practices.

Skills

Python

PyTorch

TensorFlow

MLOps

AWS

LLMs

Agentic workflows

APIs

Education

Bachelor's degree in AI Engineering

Master’s degree in AI Engineering

Tools

Hugging Face

AWS Bedrock

Bedrock AgentCore

Strands

SAP

Salesforce

Job description

MSA Safety is seeking an AI Engineer to help design, build, and scale AI-enabled applications that enable intelligent search and agentic workflows. You will orchestrate tools, deploy models in the cloud, and iterate with real-world feedback.

The right candidate will have hands-on experience with ML frameworks, MLOps, and cloud infrastructure, plus strong communication skills to translate complex ideas to stakeholders across the organization.