At Ford Motor Credit, the business problem is to enhance customer outcomes, mitigate financial risk, and boost operational efficiency by designing, building, and operationalizing scalable AI systems and intelligent agents.
Requirements
- 3+ years of applied ML/AI experience with at least 1 years focused on production ML systems and engineering.
- Strong software engineering skills: advanced Python, modular design, testing, typed codebases and deployment experience.
- Production ML/MLOps experience: containerization (Docker), orchestration (Kubernetes), CI/CD tooling, infra-as-code (Terraform/CloudFormation), and deployment frameworks (TFX, MLflow, KServe/Seldon/BentoML or equivalent).
- Practical experience with modern ML frameworks: scikit-learn, PyTorch or TensorFlow.
- Hands-on experience designing, building, and productionizing AI agents using modern frameworks (e.g., LangChain, Google ADK, LlamaIndex or similar).
- Strong data engineering skills: SQL, data modeling, experience with big data platforms (Spark, Databricks, Snowflake, BigQuery or similar) and streaming/event-driven systems.
- Knowledge of model explainability and fairness tooling (SHAP, LIME, integrated gradients or equivalent) and experiment design / A/B testing methodologies.
Responsibilities
- Lead architecture, design, and implementation of production-grade ML/AI systems, data pipelines, and intelligent agents to meet business and regulatory objectives across credit products.
- Translate business problems into engineering solutions: define success metrics, SLAs, evaluation protocols, and experimentation plans focused on measurable business impact.
- Design, prototype, validate, and productionize AI agents (conversational agents, task-execution agents, and orchestration workflows) that safely automate business processes, augment agent workflows, and integrate with backend systems.
- Own full model and agent lifecycle: data ingestion and lineage, feature engineering, model and policy development, agent orchestration, deployment, monitoring, and automated retraining.
- Collaborate to build and maintain robust MLOps and agent-ops practices: containerized agent runtimes, CI/CD for models and agent components, infra-as-code, canary/blue-green releases, and safe rollout strategies.
- Implement monitoring and observability for models and agents (performance, task success, hallucination/safety metrics, drift, data quality, latency); create incident playbooks and operationalize retraining, rollbacks, and human handoff procedures.
- Design and run rigorous evaluation frameworks for agents: scenario-based testing, simulation, backtests, holdouts, cross-validation, A/B and uplift testing, and business-impact estimation.
Other
- Bachelors degree
- Visa sponsorship is not available for this position.
- Candidates for positions with Ford Motor Company must be legally authorized to work in the United States.
- We are an Equal Opportunity Employer.
- Paid time off and the option to purchase additional vacation time.
