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.
What you'll do...
- 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.
