Job Summary
The Product Engineering Specialist – Applied AI Specialist is responsible for driving the development, deployment, and adoption of Artificial Intelligence (AI) and Generative AI (GenAI) solutions across engineering and technical functions. This role combines strong product engineering expertise with advanced AI capabilities to design, develop, validate, and scale innovative solutions that address complex business and engineering challenges.
Working as a technical specialist, the incumbent serves as a key liaison between engineering, product teams, IT, and business stakeholders to deliver AI-enabled solutions throughout the product lifecycle. The role also supports the establishment and growth of the Tech AI Innovation Lab, enabling experimentation, capability building, and enterprise-scale AI adoption while ensuring alignment with Cummins engineering, governance, security, and quality standards.
Key Responsibilities
AI Solution Development & Product Engineering
- Design, develop, validate, and deploy AI/GenAI-based solutions, proof-of-concepts, pilots, and production-ready applications.
- Apply engineering principles, analytical methods, and AI technologies to solve product, system, and business challenges.
- Lead technical problem-solving activities and provide recommendations that support quality product and business decisions.
- Support product development activities across the entire lifecycle, from concept through deployment and continuous improvement.
- Utilize structured engineering processes, including Engineering Standard Work (ESW), Failure Mode and Effects Analysis (FMEA), design reviews, and problem-solving methodologies.
TechAI Lab Enablement & Innovation
- Establish, operationalize, and scale the TechAI Innovation Lab environment, including datasets, models, tools, and development frameworks.
- Develop reusable AI architecture, accelerators, best practices, and reference implementations.
- Drive innovation through experimentation, emerging AI technologies, and advanced analytics capabilities.
- Support the evaluation and adoption of new AI tools, platforms, and methodologies.
AI Product ionization & Integration
- Lead the transition of AI solutions from proof-of-concept to pilot and enterprise-scale deployment.
- Collaborate with IT, engineering, and business teams to define deployment architectures, APIs, pipelines, integrations, and hosting strategies.
- Ensure solutions align with enterprise architecture, cybersecurity, governance, compliance, and operational requirements.
- Support deployment, monitoring, maintenance, and long-term sustainment of AI applications.
Cross-Functional Collaboration & Technical Leadership
- Serve as a technical advisor and liaison between engineering, IT, product development, and business stakeholders.
- Influence technical direction and support decision-making across projects and initiatives.
- Coordinate activities among engineers, analysts, data scientists, and technical teams to achieve project objectives.
- Mentor and guide less experienced engineers and technical professionals.
Capability Building & AI Adoption
- Coach teams and stakeholders on AI technologies, methodologies, and best practices.
- Conduct workshops, training programs, technical sessions, and office hours to accelerate AI adoption.
- Support the development of AI playbooks, standards, and reusable learning assets.
- Promote responsible AI practices and enterprise-wide adoption of AI-driven solutions.
Documentation & Continuous Improvement
- Create and maintain technical documentation, knowledge repositories, deployment guides, and solution architectures.
- Capture lessons learned and promote knowledge sharing across teams.
- Drive continuous improvement of engineering processes, AI development practices, and solution delivery methods.
Competencies
Product Engineering Competencies
- Product Development Execution, Monitoring and Control
- Product Failure Mode Avoidance
- Product Function Modeling, Simulation and Analysis
- Product Interface Management and Integration
- Product Problem Solving
- Product Verification and Validation Management
- System Requirements Engineering
- Product Configuration and Change Management
- Technical Documentation
AI & Digital Engineering Competencies
- Applied Artificial Intelligence and Machine Learning
- Generative AI and Large Language Models (LLMs)
- AI Solution Architecture and Design
- Data Science and Advanced Analytics
- MLOps and LLMOps Practices
- AI Governance and Responsible AI
- Enterprise AI Integration and Deployment
- Cloud-Based AI Platforms and Services
Leadership & Professional Competencies
- Builds Networks
- Communicates Effectively
- Decision Quality
- Drives Results
- Manages Complexity
- Resourcefulness
- Values Differences
- Coaching and Mentoring
- Cross-Functional Collaboration
- Innovation and Continuous Improvement
Qualifications
Required
- Bachelor’s degree in Engineering, Computer Science, Information Technology, Data Science, Artificial Intelligence, Electronics Engineering, or another relevant STEM discipline.
- Relevant engineering experience with demonstrated success in technical problem-solving, solution development, and project execution.
- Ability to work independently and lead technical initiatives with minimal supervision.
Preferred
- Master’s degree in Computer Science, Artificial Intelligence, Data Science, Information Technology, Engineering, or a related field.
- Certifications in AI, Machine Learning, Cloud Technologies, Data Science, or related disciplines.
- Exposure to enterprise AI governance, cybersecurity, and responsible AI frameworks
Skills Required
Technical Skills
- Strong programming expertise in Python.
- Experience building AI, Machine Learning, and Generative AI solutions.
- Experience with Large Language Models (LLMs), prompt engineering, retrieval-augmented generation (RAG), and agent-based systems.
- Knowledge of machine learning frameworks and modern AI development toolchains.
- Experience building web applications, APIs, microservices, and enterprise integrations.
- Understanding of cloud AI platforms such as Azure AI Foundry, Azure OpenAI, AWS Bedrock, or similar technologies.
- Knowledge of MLOps and LLMOps practices, including CI/CD, monitoring, model lifecycle management, and deployment automation.
- Experience with data engineering, data pipelines, and AI solution architecture.
Professional Skills
- Strong analytical and problem-solving capabilities.
- Excellent communication and stakeholder management skills.
- Ability to influence cross-functional teams and drive alignment.
- Strong coaching, mentoring, and capability-building skills.
- Project planning, execution, and delivery management.
- Documentation and knowledge-sharing capabilities.
Experience Required
- Typically 10–12 years of engineering experience , including significant exposure to AI, Machine Learning, Data Science, Software Engineering, or Digital Engineering domains.
- Proven experience delivering AI/ML/GenAI solutions from concept through production deployment.
- Experience leading technical initiatives and coordinating cross-functional teams.
- Demonstrated success in developing scalable AI solutions and integrating them with enterprise systems.
- Experience working with engineering, product development, and IT organizations in a global environment.
- Exposure to AI governance, compliance, responsible AI practices, and enterprise deployment frameworks is preferred.
- Experience coaching teams and driving organizational AI adoption initiatives is highly desirable.
Job Engineering
Organization Cummins Inc.
Role Category On-site with Flexibility
Job Type Exempt - Experienced
ReqID 2430844
Relocation Package No
100% On-Site No
