Only W2, No C2C - 4 Days onsite at Dearborn, MI Skills Required: Technical Communication, Communications, Google Cloud Platform, TensorFlow, Data Governance, Machine Learning, Python, Artificial Intelligence & Expert Systems 1. Technical Communication - 2-5 years translating complex technical concepts - such as ML model behavior, data pipeline architecture, or platform design decisions - into clear documentation, proposals, and presentations for both technical and non-technical audiences including engineering leads and product stakeholders. 2. Communications - 2-5 years of demonstrated ability to communicate effectively across cross-functional teams, including facilitating technical discussions, contributing to design reviews, and keeping stakeholders aligned on project status, risks, and decisions. 3. Google Cloud Platform - 2-5 years of hands-on experience with GCP services relevant to AI/ML and data workloads, including Vertex AI, BigQuery, GCS, Dataflow, or Cloud Composer, with the ability to deploy and manage workloads in a production cloud environment. 4. TensorFlow - 2-5 years building, training, and evaluating machine learning models using TensorFlow or TensorFlow Extended (TFX), including experience with model versioning, pipeline integration, and deploying models to production serving infrastructure. 5. Data Governance - 2-4 years applying data governance principles including data lineage, access controls, metadata management, and compliance standards to ensure telemetry and ML datasets meet quality, security, and regulatory requirements. 6. Machine Learning - 3-5 years of applied ML experience including feature engineering, model selection, training, validation, and deployment. Candidate should be comfortable working with both structured and unstructured data in the context of real-world engineering or automotive telemetry use cases. 7. Python - 3-5 years writing production-quality Python for data engineering, ML pipeline development, or platform tooling. Proficiency with relevant libraries such as Pandas, NumPy, scikit-learn, and TensorFlow is expected, along with familiarity with code quality practices such as testing and version control. 8. Artificial Intelligence & Expert Systems - 3-5 years of experience designing or working with AI systems, including the application of large language models, expert systems, or intelligent automation within developer or data workflows. Candidate should understand model lifecycle management, prompt engineering, and responsible AI practices. Experience Required: 5 or more 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, with specific preference for Google Cloud Vertex AI or equivalent managed ML platforms  Demonstrated experience building agentic AI systems using frameworks such as LangChain, LangGraph, Google Agent Builder, or equivalent orchestration tooling  Proficiency in Python and ML development tooling including Hugging Face, PyTorch or TensorFlow, and MLflow or Vertex AI Experiments  Experience designing and evaluating LLM outputs for production systems, including prompt engineering, retrieval-augmented generation (RAG) architectures, and model evaluation metrics  Strong understanding of MLOps practices including model versioning, deployment pipelines, monitoring, and retraining workflows on GCP  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 Education Required: Bachelor's Degree Additional Information : ***HYBRID / 4 days per week in the office)