Digital Product Engineer - Machine Learning & AI

International Motors, LLC•Lisle, IL

18h•Hybrid

About The Position

International is undergoing a transformation from being a supplier of trucks, buses, and engines to becoming a provider of complete and sustainable transport solutions. As part of this journey, we are investing heavily in digital capabilities, data products, and AI-enabled tools. We have an exciting opportunity for a Digital Product Engineer - Machine Learning / AI. This role sits at the intersection of Product, Design, and hands-on ML/ AI engineering, and is responsible for design, development, deployment and operation of production-grade ML systems at scale. This role focuses on bridging the gap between data science and at-scale, production ready solutions – turning data and models into reliable, observable, and secure products. In this role you will work following a product-led approach, including close collaboration with product owner, data scientists, software engineers, and other cross-functional team members/ stakeholders to operationalize machine learning solutions end-to-end, with a strong emphasis on MLOps, cloud infrastructure, data platforms, and product engineering excellence. We are looking for someone with a strong sense of ownership, curiosity, and a solution-orientated mindset of “we may not have done this before—let’s figure it out and learn quickly.” This position will be based in Lisle, Illinois with hybrid in-office and remote work flexibility.

Requirements

Bachelor's degree and at least 9 years of Information Technology or IT architecture experience
At least 2 years of lead experience
OR
Master's degree and at least 5 years of Information Technology or IT architecture experience
At least 2 years of lead experience
OR
At least 12 years of Information Technology or IT architecture experience
At least 2 years of lead experience
2-3 Years experience building and deploying ML systems using cloud infrastructure, especially in product-led, transformation environments (e.g. Azure ML, Compute, AKS, Azure Storage, Databricks, Key Vault, Palantir, etc.)
1-2 years of MLOps Experience, including DevOps-CI/CD for ML workflows, model and experiment versioning, monitoring models, performance and drift
2-3+ years of progressively responsibility delivering technical solutions in Product-led teams, or similar role using an Agile/ Scrum environment. Comfort with modern product practices, including MVP definition, experimentation, metrics, feedback loops, and iterative delivery.
Qualified candidates, excluding current employees, must be legally authorized on an unrestricted basis (US Citizen, Legal Permanent Resident, Refugee or Asylee) to be employed in the United States. We do not anticipate providing employment related work sponsorship for this position (e.g., H-1B status)

Nice To Haves

Bring a passion for working to understand needs and challenges with a customer-centric mindset. Have prior experience and skills in gathering and analyzing Voice of Customer, rapid innovation and design-thinking.
Demonstrate initiative, with the ability to work independently when needed, and pragmatic problem-solving abilities. Experience following a strong analytic approach to evaluate complex choices, identify issues, and propose effective solutions and decisions.
Excellent written and verbal communication skills for interacting with users, vendors, and other IT professionals. Ability to translate complex technical topics into easy-to-understand information with a strong attention to detail.
Production-grade Python and ML engineering experience
Working knowledge of common data engineering, ML /AI platforms and software solutions, including ChatGPT, Claude and Copilot, Databricks, Palantir AI-Foundry, LangChain or similar LLM orchestration frameworks, and Kubernetes and/or similar container technologies
Experience deploying ML models as scalable APIs or Services

Responsibilities

Build, deploy and maintain production ML systems and solutions end-to-end (training > deployment > monitoring).
Partner with data scientists to operationalize models for real-world use
Take a hands-on role in building and iterating solutions, turning ideas into working prototypes and production capabilities.
Define and Implement ML Ops best practices, including DevOps and CI/CD, versioning and monitoring / telemetry.
Deploy models as scalable, reliable solutions – part of APIs, headless services, APIs, or embedded capabilities.
Monitor model performance, data quality, and drive retaining strategies.
Partner with engineering, architecture, UX, and data science teams to translate models and insights into usable, scalable product experiences.
Help contribute to the product goals and vision, roadmap, and delivery plan for ML/AI-driven capabilities by identifying key problems, decisions to improve, required data, and expected business outcomes.
Work in multi-disciplined product teams and with cross-functional stakeholders (internally and externally as needed).
Actively support delivery by clarifying requirements, making trade-offs, and unblocking progress across stakeholders.
Contribute to a team culture that values curiosity, collaboration, pragmatism, and continuous learning.
Ensure solutions meet standards for security, reliability and maintainability
Ensure AI capabilities are practical, trusted, and responsibly deployed through appropriate guardrails, transparency, and human-in-the-loop design.