Staff AI Engineer

Mission

Design and deliver trustworthy AI systems, machine-learning solutions, data products, and applications that improve decisions, automate work, and create measurable business impact across Sales, Service, Marketing, and Global Markets. This is a hands-on Staff AI Engineer role for an experienced individual contributor who can move from business problem framing and analytical discovery through data engineering, model and AI-system development, application delivery, and production operation. The role combines practical data science with strong software and platform-engineering skills to build reusable, secure, maintainable, and adopted AI capabilities at scale.

Key Responsibilities

Applied AI and Machine Learning

Translate ambiguous business problems into clear AI objectives, solution strategies, evaluation plans, and measurable success criteria. Develop, validate, and improve predictive, forecasting, classification, segmentation, optimization, natural-language, generative-AI, and decision-support solutions. Select practical statistical, machine-learning, and AI techniques based on the business decision, available data, operational constraints, risk, and expected value. Apply methods such as experimentation, time-series forecasting, causal analysis, retrieval-augmented generation, agentic workflows, or other modern AI patterns when they are fit for purpose. Partner with stakeholders to identify when AI is appropriate—and when simpler analytics, rules, or workflow automation will create better outcomes.

Data and Feature Engineering
Define data requirements and partner with data engineering, platform, and business teams to establish reliable, documented data sources. Build scalable ingestion, transformation, aggregation, feature, embedding, and knowledge pipelines using batch or streaming patterns as appropriate. Create reusable data products, analytical datasets, semantic models, feature assets, and retrieval indexes that support multiple use cases. Perform exploratory analysis, data-quality assessment, schema validation, feature selection, leakage detection, and data-availability analysis. Work across structured and unstructured data, including customer, vehicle, dealer, sales, service, warranty, incentive, document, conversation, and operational datasets.

AI Application and Platform Engineering
Build production-grade AI applications, services, APIs, assistants, dashboards, and workflow tools that make AI capabilities accessible to end users. Translate models and AI systems into intuitive user experiences, decision-support tools, automation workflows, and self-service capabilities. Design clean interfaces between data pipelines, models, foundation models, tools, backend services, and front-end or workflow applications. Implement retrieval, grounding, tool use, orchestration, prompt management, evaluation, and human-in-the-loop patterns where appropriate. Develop secure, testable, observable, and maintainable software using sound engineering practices, code review, version control, and automated testing. Partner with architects and software engineers on application structure, integration patterns, authentication, authorization, deployment, and operational support.

Evaluation, Reliability, and Production AI
Establish evaluation frameworks that reflect real-world business outcomes, not only offline technical metrics. Assess model and AI-system quality, calibration, bias, interpretability, robustness, factuality, groundedness, safety, usability, latency, and operational fit. Design and analyze experiments, pilots, red-team exercises, and champion/challenger approaches to validate value and manage risk before broad adoption. Deploy solutions as APIs, batch processes, scheduled workflows, applications, assistants, or decision-support capabilities. Establish practices for dependency management, versioning, data lineage, prompt and model tracking, release management, rollback, and reproducibility. Design monitoring for data quality, drift, model performance, response quality, safety signals, application health, latency, availability, usage, and business outcomes. Investigate production issues, identify root causes, and improve models, pipelines, services, prompts, and user experiences through structured iteration.

Responsible AI, Security, and Governance
Apply responsible-AI, privacy, security, and data-governance principles throughout solution design and operation. Define appropriate controls for access, data handling, auditability, explainability, human oversight, model and prompt risk, and use of external or third-party services. Document assumptions, limitations, intended use, prohibited use, evaluation results, and operational ownership for AI solutions. Partner with security, privacy, architecture, legal, and governance stakeholders to support compliant enterprise deployment.

Technical Leadership and Enablement
Serve as a technical authority and trusted advisor on applied AI, machine learning, data engineering, application development, and AI platform patterns. Raise the quality bar through reusable patterns, reference implementations, code reviews, documentation, testing, observability, and reproducibility. Coach data scientists, analysts, engineers, and citizen builders on sound modeling, data, software-development, and responsible-AI practices. Help teams evaluate and use platforms such as Databricks, Azure AI, Azure AI Foundry, Glean, and other enterprise tooling when they accelerate delivery without compromising quality or governance. Share lessons learned, reusable components, reference architectures, evaluation methods, and practical guidance across the AI Center and partner organizations.

Required Qualifications

• Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field; advanced degree preferred.
• 8+ years of professional experience across artificial intelligence, machine learning, data science, data engineering, software engineering, application development, or a closely related discipline.
• Demonstrated experience taking AI, machine-learning, analytics, or data-product solutions from problem definition and proof of concept through production deployment and ongoing operation.
• Strong proficiency in Python and SQL, including production-quality code, testing, version control, documentation, and debugging.
• Hands-on experience with data-science and machine-learning libraries such as Pandas, NumPy, scikit-learn, PyTorch, TensorFlow, or equivalent technologies.
• Experience building or integrating AI applications, APIs, services, data pipelines, applications, dashboards, or workflow tools.
• Experience with model development, feature engineering, experiment design, statistical analysis, AI evaluation, and communicating results to nontechnical audiences.
• Experience deploying and operating solutions through APIs, batch pipelines, notebooks-to-production workflows, applications, assistants, or comparable production patterns.
• Practical understanding of software and data engineering practices, including CI/CD, automated testing, observability, dependency management, data quality, and release management.
• Experience working with large-scale data platforms such as Databricks, Spark/PySpark, cloud data warehouses, or equivalent technologies.
• Demonstrated ability to operate independently, make sound technical tradeoffs, and deliver in a fast-changing, cross-functional environment.

Preferred Qualifications

• Master’s or PhD in Computer Science, Statistics, Machine Learning, Operations Research,
• Mathematics, Engineering, or a related quantitative field.
• Experience in automotive, sales, service, marketing, customer analytics, dealer analytics, warranty, incentives, forecasting, or other operationally complex domains.
• Experience building internal AI platforms, developer tools, data applications, decision-support products, assistants, or enterprise workflow solutions.
• Experience with large language models, prompt engineering, retrieval-augmented generation, embeddings, vector search, tool calling, agentic workflows, or AI application evaluation.
• Experience with causal inference, time-series forecasting, optimization, recommendation systems, natural-language processing, or generative-AI-enabled workflows.
• Experience with MLflow or comparable tools for experiment tracking, model registry, evaluation, and lifecycle management.
• Experience with Azure, Azure AI Foundry, Databricks, REST APIs, containerized deployment, CI/CD, cloud-native data services, and application observability.
• Experience defining model governance, responsible-AI controls, data governance, security controls, interpretability practices, or risk-based evaluation standards.
• Experience quantifying financial impact and partnering with Finance or business leaders to validate value realization.
• Familiarity with enterprise AI and developer platforms, including Glean, Azure AI Foundry, Databricks, GitHub, or comparable platforms.

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