AI/ML Engineer at Ford Global Career Site

Dearborn, Michigan, United States -

Full Time

Start Date

Immediate

Expiry Date

19 Mar, 26

Salary

0.0

Posted On

19 Dec, 25

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

AI/ML Systems, Data Pipelines, MLOps, Python, SQL, Big Data Platforms, Model Monitoring, Explainability, Fairness Assessments, Agent Architectures, Experiment Design, Production ML Systems, Data Quality Checks, Regulated Environments, Communication Skills, Software Engineering

Industry

Motor Vehicle Manufacturing

Description

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. Drive responsible AI: implement explainability, fairness assessments, access controls, data minimization, privacy safeguards, and mitigation plans for bias, safety or other harms from agent outputs. Architect guardrails for autonomous agents (authorization, scope-limiting, confirmation flows, human-in-the-loop escalation, sandboxing, and cost controls). Lead cross-functional stakeholder communication: present technical trade-offs, agent capabilities and limitations, risk assessments and outcomes to product owners, legal, risk/compliance and senior leadership. Continuously evaluate and recommend improvements to AI safety guardrails, agent orchestration tooling and the SDLC to accelerate delivery while maintaining safety/compliance. Established and active employee resource groups Bachelors degree 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. 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), including RAG/embedding pipelines, memory/state management, tool-using agent patterns, vector DB integration, secure connector design, and operational guardrails for safe human-in-the-loop orchestration. Experience designing or productionizing agent architectures: tool-using agents, planner/actor patterns, memory/state management, and safe orchestration of multi-step tasks. Strong data engineering skills: SQL, data modeling, experience with big data platforms (Spark, Databricks, Snowflake, BigQuery or similar) and streaming/event-driven systems. Demonstrated ability to design and implement model and agent monitoring, data quality checks, drift detection and alerting. Knowledge of model explainability and fairness tooling (SHAP, LIME, integrated gradients or equivalent) and experiment design / A/B testing methodologies. Experience working in regulated environments (credit/finance preferred) and producing artifacts for model risk, audit, and compliance. Strong verbal and written communication skills with experience distilling complex technical topics for non-technical stakeholders.

Responsibilities

Lead the architecture, design, and implementation of production-grade ML/AI systems and data pipelines. Collaborate to build and maintain robust MLOps practices and implement monitoring for models and agents.