Senior AI/ML Engineer

Ford

1d • Hybrid

About The Position

We made history and now we work to transform the future – for our customers, our communities and our families. You'll see your work on the road every day, helping people move freely and pursue their dreams. At Ford, you can build more than vehicles. Come build what matters.
The Ford Motor Credit Company team helps put people behind the wheels of great Ford and Lincoln vehicles. By partnering with dealerships, we provide financing, personalized service and professional expertise to thousands of dealers and millions of customers in over one hundred countries around the world.
In this position...
The Senior AI/ML Engineer at Ford Credit is a hands-on technical role responsible for designing, developing, and operationalizing advanced analytics and AI solutions that materially improve customer outcomes, reduce financial risk, and increase operational efficiency across Ford Credit. You will deliver across a diverse portfolio of strategic AI initiatives including real-time and batch fraud detection (traditional ML and graph/anomaly approaches), hierarchical forecasting, and GenAI capabilities such as conversational agent-assist, RAG-based knowledge grounding, and tool-using AI agents that automate business processes.
You will partner closely with product, engineering, data engineering, risk/compliance, and business stakeholders to define success criteria, design reproducible data and model pipelines, prototype and productionize solutions, and implement monitoring, explainability, and governance to meet security and regulatory requirements. You will own end-to-end delivery for assigned projects, balancing rapid experimentation with production rigor to produce measurable business outcomes. This role requires strong technical judgment, clear stakeholder communication, and a commitment to responsible AI practices for both classic ML and modern GenAI/agent workflows.

Requirements

• Bachelor's degree in Computer Science or a related field (e.g., Machine Learning, Statistics, Data Science, Electrical/Computer Engineering) or equivalent practical experience
• 5+ years of applied ML/AI experience (or equivalent) with demonstrated hands-on delivery in production environments.
• Strong software engineering skills in Python and modern engineering practices (testing, modular design, reliable pipelines, maintainability).
• Production ML and MLOps experience: Docker, CI/CD, model serving, monitoring/observability, and experience working with orchestrated pipelines (e.g., Kubernetes and/or equivalent).
• Strong data skills: advanced SQL and experience with big data platforms (e.g., BigQuery, Spark, Databricks, Snowflake) and/or streaming/event-driven systems.
• Traditional ML expertise: practical experience building and productionizing models for credit use cases (e.g., classification, regression, time-series forecasting, anomaly detection, graph-based fraud approaches).
• GenAI/LLM expertise: hands-on experience building and evaluating LLM-based systems, including RAG, prompt engineering, evaluation of response quality/grounding, and conversational AI/agent-assist applications.

Nice To Haves

• Master’s degree or higher in Computer Science or a related field (e.g., Machine Learning, Statistics, Data Science, Electrical/Computer Engineering) or equivalent practical experience
• AI agent engineering: hands-on experience designing and deploying tool-using AI agents with safe orchestration, memory/state handling, evaluation, and operational guardrails. Experience with popular agent frameworks is a plus.
• Evaluation & experimentation: experience with holdouts, cross-validation/backtesting, A/B testing, uplift modeling, and business impact estimation; ability to design robust evaluation for both ML and GenAI/agents.
• Model explainability & fairness: familiarity with SHAP/LIME (or equivalent) and ability to incorporate explainability and fairness into validation and documentation.
• Regulated environment experience: familiarity with governance needs for credit/financial models and producing audit-ready artifacts (model/system cards, validation reports, documentation).
• Cloud familiarity (AWS/Azure/GCP) and ML services is preferred.
• Excellent communication and collaboration with product, engineering, legal, and risk teams; ability to explain technical trade-offs and risk posture to stakeholders.

Responsibilities

• Design, prototype, validate, and productionize traditional ML models and GenAI/LLM-based solutions (including RAG and agentic workflows) to meet business objectives across credit products.
• Translate business problems into technical solutions: define metrics, success criteria, evaluation strategy, and experimentation plans for both ML models and GenAI experiences.
• Own end-to-end model/system lifecycle for assigned use cases, including data ingestion and lineage, feature engineering, model development, evaluation, deployment, monitoring, and retraining/re-optimization.
• Build and operationalize AI agents that automate business processes and augment agent-assist workflows, including: tool-using agent patterns, multi-step orchestration memory/state management and safe handoffs to humans secure connector design to upstream/downstream systems integration into APIs, microservices, or agent frameworks
• Ensure explainability, fairness, and regulatory compliance for credit/financial use cases. Produce required documentation and artifacts for model risk and audit.
• Integrate solutions into production by collaborating with software engineers and MLOps teams (e.g., model/LLM services, RAG services, agent runtimes) and supporting CI/CD for ML/GenAI components.
• Instrument monitoring and observability across the full lifecycle: traditional ML: performance, drift, data quality, latency GenAI/agents: quality/safety metrics, grounding/citation quality, tool-call reliability, latency/cost, and drift in retrieval inputs define retraining and incident playbooks, and lead mitigation/rollback actions.
• Build robust evaluation pipelines for both paradigms: holdout strategies, cross-validation, backtesting uplift/A-B testing frameworks and business impact estimation scenario-based and simulation-based testing for agent behavior and GenAI responses.
• Drive responsible AI and safety for agents/LLMs, including guardrails (authorization/scope limits, confirmation flows, human-in-the-loop escalation), bias mitigation, and privacy-by-design (PII handling).
• Communicate clearly with non-technical stakeholders and senior leadership—articulating model/system behavior, limitations, risks, and measurable outcomes.
• Mentor and raise team capability in reproducible ML engineering and agent/GenAI development practices.
• Coordinate with data engineering to ensure reliable, documented data sources and lineage.
• Keep abreast of emerging AI safety practices and recommend improvements to guardrails and SDLC processes.

Benefits

• Immediate medical, dental, vision and prescription drug coverage
• Flexible family care days, paid parental leave, new parent ramp-up programs, subsidized back-up child care and more
• Family building benefits including adoption and surrogacy expense reimbursement, fertility treatments, and more
• Vehicle discount program for employees and family members and management leases
• Tuition assistance
• Established and active employee resource groups
• Paid time off for individual and team community service
• A generous schedule of paid holidays, including the week between Christmas and New Year’s Day
• Paid time off and the option to purchase additional vacation time.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 5,001-10,000 employees