Artificial Intelligence Engineer

Artificial Intelligence Engineer Dearborn, MI Job Details Information Technology • Contract • Long-term • 1 position Description Stefanini Group is hiring an Artificial Intelligence Engineer to support AI and ML engineering capability within the TOP platform, focusing on model fine‑tuning oversight, agentic orchestration architecture, and LLM evaluation. The role includes overseeing vendor fine‑tuning of Google Cloud Vertex AI using proprietary diagnostic data, ensuring compliance with IP protection requirements and model weight storage architecture. Responsibilities Design and build Orchestration Layer: integration framework connecting external AI engines with other 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 tooling for model monitoring, drift detection, and retraining triggers within GCP environment. Collaborate with data engineering team to define data preparation and feature engineering requirements that support model fine‑tuning and inference quality. Partner with GCP Cloud Engineers to ensure model artifact storage, versioning, and access controls comply with 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. Experience Required 5+ years of professional experience in machine learning engineering, AI systems development, or applied AI research. Hands‑on experience fine‑tuning LLMs in a cloud environment (Google Cloud Vertex AI or equivalent). Demonstrated experience building agentic AI systems using LangChain, LangGraph, Google Agent Builder or equivalent tooling. 3–5 years of applied ML experience including feature engineering, model selection, training, validation, and deployment; comfortable with structured and unstructured data in automotive telemetry or similar contexts. 3–5 years of production‑quality Python for data engineering, ML pipelines, or platform tooling; proficiency in Pandas, NumPy, scikit‑learn, TensorFlow; familiarity with testing and version control. 3–5 years designing or working with AI systems applying large language models, expert systems, or intelligent automation; understanding of model lifecycle management, prompt engineering, and responsible AI practices. Proficiency in Python and ML tooling such as Hugging Face, PyTorch, TensorFlow, MLflow, Vertex AI Experiments. Experience designing and evaluating LLM outputs, prompt engineering, retrieval‑augmented generation (RAG) architectures, and model evaluation metrics; strong MLOps practices on GCP. Experience working in regulated or IP‑sensitive environments where model artifact ownership and data governance are active concerns. 2–5 years of hands‑on GCP experience with Vertex AI, BigQuery, GCS, Dataflow, Cloud Composer; ability to deploy and manage workloads in production. Experience Preferred Automotive diagnostics, vehicle telematics, or connected vehicle platforms; familiarity with Diagnostic Trouble Code (DTC) data, OTA update systems, or repair order (RO) data structures. Experience with multi‑agent AI systems and tool‑use patterns in production. Google Cloud Professional Machine Learning Engineer certification. Education Required Bachelor's Degree. Additional Information Hybrid role: 4 days per week in the office. Pay Range: $76.00 – $81.00 per hour. #J-18808-Ljbffr