Senior AI/ML Engineer

International Motors, LLC•Lisle, IL

About The Position

The Senior AI/ML Engineer designs, develops, deploys, and operates production-grade artificial intelligence and machine learning solutions intended to deliver measurable business outcomes. The position combines machine learning expertise, software engineering discipline, and solution architecture to translate business needs into secure, scalable, supportable, and responsible AI capabilities. The role provides technical leadership throughout the solution lifecycle, from discovery and architecture through data preparation, model development, evaluation, deployment, monitoring, and continuous improvement. Within a federated AI Center of Excellence model, the engineer partners with data engineering, analytics, product, architecture, cybersecurity, and business teams to deliver value through scalable AI and ML models. The engineer owns end-to-end solution delivery and production operation, including model and data versioning, reproducible pipelines, automated deployment, monitoring, retraining, release and lifecycle practices, and production support. The role also engineers generative AI solutions, aligns AI/ML data work with enterprise data and analytics systems, applies governance and responsible AI practices, works with stakeholders to assess business value and solution fit, and provides technical leadership and mentorship.

Requirements

Bachelor's degree and at least 2 years of software engineer experience, or a master's degree and at least 1 year of software engineer experience, or at least 5 years of software engineer experience.
Qualified candidates, excluding current employees, must be legally authorized on an unrestricted basis as a US Citizen, Legal Permanent Resident, Refugee, or Asylee to work in the United States.
Demonstrated experience developing maintainable Python software and deploying AI/ML solutions into production environments.
Experience developing tested, maintainable Python applications and using common machine learning frameworks such as scikit-learn, TensorFlow, or PyTorch.
Experience with model deployment, model serving, CI/CD, experiment tracking, versioning, monitoring, and retraining workflows.
Experience with cloud-based AI/ML platforms, containerized deployments, APIs, distributed data processing, and enterprise data ecosystems.
Experience working across business, product, data, engineering, architecture, and risk teams in an enterprise environment.

Nice To Haves

Master’s degree in a quantitative or technical field.
Five years of relevant experience working as a Data Scientist, Data Engineer, Machine Learning Engineer, or AI Engineer is preferred.
Experience with production AI/ML solution delivery.
Seven years of relevant professional experience, including demonstrated ownership of production AI/ML systems, is preferred.
Experience designing or operating generative AI solutions, including retrieval-augmented generation and systematic evaluation.
Familiarity with enterprise architecture, cybersecurity, privacy, responsible AI, and model governance practices.

Responsibilities

Design, build, validate, and deploy machine learning models for complex business use cases.
Lead feature engineering, model selection, tuning, validation, and production-readiness assessments.
Select effective approaches based on business requirements, data availability, explainability, operational constraints, and measurable performance.
Define model acceptance criteria and implement reproducible offline and production evaluations.
Assess model robustness, bias, explainability, limitations, and failure modes appropriate to the use case.
Own end-to-end delivery of AI/ML solutions from discovery and prototyping through production deployment and ongoing operation.
Implement model and data versioning, experiment tracking, reproducible pipelines, automated deployment, and rollback practices.
Establish monitoring for data quality, data drift, model performance, reliability, latency, utilization, and cost.
Define retraining, release, environment-promotion, and lifecycle-management approaches.
Provide production support, perform root-cause analysis, and drive corrective and preventive actions for operational issues.
Design and operationalize generative AI solutions using fit-for-purpose techniques such as retrieval-augmented generation, prompt orchestration, tool integration, and structured output.
Implement appropriate guardrails, human oversight, content controls, and monitoring for generative AI solutions.
Evaluate foundation models and AI platforms against business, security, architecture, and governance requirements and standards.
Work with large-scale structured and unstructured data to support AI/ML development and evaluation.
Define data requirements and partner with data engineering teams to improve data quality, availability, lineage, and observability.
Develop AI/ML-specific data transformations and features while partnering with data engineers on shared, governed, production-grade pipelines.
Contribute to curated data assets and integrate AI capabilities with enterprise analytics, business intelligence, and operational systems.
Ensure adherence to enterprise AI governance, security, and compliance standards.
Apply responsible AI practices, including documentation, explainability, and risk considerations.
Contribute to the development and refinement of AI/ML best practices and reusable patterns.
Support model documentation and auditability requirements.
Partner with stakeholders to turn ambiguous business problems into testable hypotheses, technical requirements, and delivery plans.
Define technical and business success measures with product owners and business partners.
Assess whether AI/ML is appropriate for a use case and recommend conventional analytics, rules-based, or software approaches when more suitable.
Estimate delivery complexity, dependencies, operational impact, and technical risk.
Serve as technical lead for medium-to-large AI/ML initiatives and drive resolution of complex implementation issues.
Mentor junior team members.
Review code, models, technical designs, and operational plans to improve quality and consistency.