AI Engineer

About the Role

As PennEngineering accelerates its Speed of Now transformation (respond in 1 hour, quote in 1 day, samples in 1 week, finished product in 1 month), we are building an internal capability to design, develop, and deploy AI-powered workflows, automation, and agentic solutions that improve speed, consistency, and quality across the business.

The AI Engineer is responsible for converting validated business user stories into production-ready AI-powered workflows and AI agents. Some use cases will be addressed through AI-driven workflow automation, while others will require agentic AI. This role operates as part of a cross-functional delivery model, supported by solution architecture, IS/IT engineering, DevOps, as well as engagement with business stakeholders and subject-matter experts.

Key Responsibilities

1. AI Development

• Partner with the business to identify AI use cases and opportunities
• Design, build, and deploy AI-powered workflows, automation, and agentic solutions
• Convert user-stories into structured functional designs for AI workflows and agents
• Develop secure and scalable agents using AWS technologies up to and including AWS Bedrock and AWS Quick Suite
• Maintain an internal library of AI-powered workflows and agents with version control, telemetry, and continuous improvement processes

2. Agile Delivery

Operate in an iterative agile model:

• User-story intake and prioritization
• AI workflow or agent design and build
• Testing and validation with business SMEs
• Pilot deployment and data-driven refinement
• Establish a predictable pipeline and regular release cadence
• Ensure traceability from user story to AI solution to business outcome

3. Engineering & Integration

• Work with the IS team to ensure access to clean, governed, structured data where required
• Collaborate with IS solution architects to align designs with enterprise standards
• Integrate AI solutions with existing enterprise systems and platforms
• Ensure all AI-powered workflows and agents meet corporate security, compliance, and audit requirements

Key KPIs

• Number of AI powered workflows, automation and agents deployed tied to validated user stories and process improvements
• Cycle-time reduction across targeted processes
• % of user stories converted into deployable AI solutions each quarter
• Adoption rate of AI-powered workflows and agents among internal users
• Measured impact on customer experience, throughput, accuracy, or friction removal
• Time-to-deploy (elapsed from user-story sign-off to production launch)

What does success look like?

Success in this role is when PennEngineering sees a steady, predictable pipeline of working AI powered workflows, automation and agents that meaningfully improve our employee and customer experience. Most importantly, the role is an accelerator of the companys AI transformation. You are proactive, iterative, customer-obsessed, data-driven, and blend technical excellence with business impact.

The AI Engineer contributes to PennEngineerings broader AI capability by delivering reliable, secure, and practical AI solutions grounded in real business needs, working collaboratively across technology and business teams.

Required Qualifications

• 3+ years of experience in software engineering, with 1+ years focused on AI/ML development
• Working knowledge in Python and modern AI/ML frameworks (e.g., LangChain, LlamaIndex, Hugging Face)
• Proven experience with cloud platforms, particularly AWS (Bedrock, SageMaker, Lambda, etc.)
• Deep understanding of LLMs, prompt engineering, RAG architectures, and agentic AI patterns
• Experience with MLOps practices, CI/CD pipelines, and production deployment
• Knowledge of data engineering, APIs, and system integration
• Strong communication skills
• Bachelor's degree in Computer Science, Engineering, or related field

Preferred Qualifications

• Experience with AWS Quick Suite or similar enterprise AI platforms
• Experience in manufacturing, supply chain, or industrial environments
• Familiarity with agile methodologies and product development practices