# Machine Learning and AI Developer

**Company**: Ford Motor Company
**Location**: Dearborn, MI
**Work arrangement**: hybrid
**Experience**: senior
**Job type**: full-time
**Salary**: $85,400 - $192,900
**Category**: IT
**Industry**: Automotive
**Wikidata**: https://www.wikidata.org/wiki/Q44294

**Apply**: https://efds.fa.em5.oraclecloud.com/hcmUI/CandidateExperience/en/sites/CX_1/job/62235?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_3705dd85-6a8

## Description

Ford’s Electric Vehicles, Digital and Design (EVDD) team is charged with delivering the company’s vision of a fully electric transportation future. EVDD is customer-obsessed, entrepreneurial, and data-driven and is dedicated to delivering industry-leading customer experience for electric vehicle buyers and owners.

In this role, you will be at the center of Ford's AI engineering capability, overseeing vendor fine-tuning operations, designing Ford's internal orchestration layer, and driving measurable improvements in AI engine performance across dealer service, manufacturing, and validation workflows.

## Responsibilities

- Support Ford's AI and ML engineering capability within the TOP platform, including model fine-tuning oversight, agentic orchestration architecture, and LLM evaluation

- Oversee vendor fine-tuning of Google Cloud Vertex AI using Ford proprietary diagnostic data, ensuring compliance with Ford's IP protection requirements and model weight storage architecture

- Design and build Ford's Orchestration Layer, the integration framework that connects external AI engine with other Ford internal AI engines and TOP platform services

- Evaluate AI engine outputs against defined accuracy, latency, and first-time fix rate metrics; drive iterative improvement through structured feedback loops

- Define model evaluation frameworks and acceptance criteria for AI-generated triage recommendations, ensuring clinical accuracy before dealer-facing deployment

- Build internal Ford tooling for model monitoring, drift detection, and retraining triggers within Ford's GCP environment

- Collaborate with Ford's data engineering team to define data preparation and feature engineering requirements that support model fine-tuning and inference quality

- Partner with the Ford GCP Cloud Engineers to ensure model artifact storage, versioning, and access controls comply with Ford's IP and security policies

- Contribute to the long-term insourcing roadmap by documenting model architectures, training pipelines, and prompt frameworks in sufficient detail to enable internal replication

- Represent AI and ML engineering in architecture reviews and vendor technical discussions.

## Qualifications

- 5+ years of professional experience in machine learning engineering, AI systems development, or applied AI research

- 3+ years Hands-on experience fine-tuning LLMs in a cloud environment, with specific preference for Google Cloud Vertex AI or equivalent managed ML platforms

- 2+ years of experience building agentic AI systems using frameworks such as LangChain, LangGraph, Google Agent Builder, or equivalent orchestration tooling

- 4+ years of Proficiency in Python and ML development tooling including Hugging Face, PyTorch or TensorFlow, and MLflow or Vertex AI Experiments

- 3+ years of experience designing and evaluating LLM outputs for production systems, including prompt engineering, retrieval-augmented generation (RAG) architectures, and model evaluation metrics

- 5+ years of Strong understanding of MLOps practices including model versioning, deployment pipelines, monitoring, and retraining workflows on GCP

- 4+ years Experience working in regulated or IP-sensitive environments where model artifact ownership and data governance are active concerns

- Strong written and verbal communication skills; ability to translate technical AI concepts for non-technical executive stakeholders

## Skills

### Required
- Python
- Google Cloud Vertex AI
- LangChain
- LangGraph
- Google Agent Builder
- Hugging Face
- PyTorch
- TensorFlow
- MLflow
- Vertex AI Experiments
- MLOps

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