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Stefanini Group is hiring!
Stefanini is looking for an Artificial Intelligence Engineer (Dearborn, MI)
For quick apply, please reach out to Fardeen Ali at /
We are seeking an AI Engineer to support 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 proprietary diagnostic data, ensuring compliance with IP protection requirements and model weight storage architecture
ResponsibilitiesDesign and build Orchestration Layer. The integration framework that connects external AI engine 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 Google Cloud Platform environment Collaborate with data engineering team to define data preparation and feature engineering requirements that support model fine-tuning and inference quality Partner with the Google Cloud Platform 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 Required5 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 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 cases3 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. 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 practicesProficiency 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 Google Cloud Platform Experience working in regulated or IP-sensitive environments where model artifact ownership and data governance are active concerns Google Cloud Platform 2 5 years of hands-on experience with Google Cloud Platform 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
Experience PreferredExperience in automotive diagnostics, vehicle telematics, or connected vehicle platforms. Familiarity with Diagnostic Trouble Code (DTC) data, Over-the-Air (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 RequiredBachelor's Degree
Additional Information:
HYBRID / 4 days per week in the office)
About Stefanini Group
The Stefanini Group is a global provider of offshore, onshore and near shore outsourcing, IT digital consulting, systems integration, application, and strategic staffing services to Fortune 1000 enterprises around the world. Our presence is in countries like the Americas, Europe, Africa, and Asia, and more than four hundred clients across a broad spectrum of markets, including financial services, manufacturing, telecommunications, chemical services, technology, public sector, and utilities. Stefanini is a CMM level 5, IT consulting company with a global presence. We are a CMM Level 5 company.
Stefanini Group is hiring!
Stefanini is looking for an Artificial Intelligence Engineer (Dearborn, MI)
For quick apply, please reach out to Fardeen Ali at /
We are seeking an AI Engineer to support 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 proprietary diagnostic data, ensuring compliance with IP protection requirements and model weight storage architecture
ResponsibilitiesDesign and build Orchestration Layer. The integration framework that connects external AI engine 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 Google Cloud Platform environment Collaborate with data engineering team to define data preparation and feature engineering requirements that support model fine-tuning and inference quality Partner with the Google Cloud Platform 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 Required5 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 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 cases3 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. 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 practicesProficiency 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 Google Cloud Platform Experience working in regulated or IP-sensitive environments where model artifact ownership and data governance are active concerns Google Cloud Platform 2 5 years of hands-on experience with Google Cloud Platform 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
Experience PreferredExperience in automotive diagnostics, vehicle telematics, or connected vehicle platforms. Familiarity with Diagnostic Trouble Code (DTC) data, Over-the-Air (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 RequiredBachelor's Degree
Additional Information:
HYBRID / 4 days per week in the office)
- Listed salary ranges may vary based on experience, qualifications, and local market. Also, some positions may include bonuses or other incentives***
About Stefanini Group
The Stefanini Group is a global provider of offshore, onshore and near shore outsourcing, IT digital consulting, systems integration, application, and strategic staffing services to Fortune 1000 enterprises around the world. Our presence is in countries like the Americas, Europe, Africa, and Asia, and more than four hundred clients across a broad spectrum of markets, including financial services, manufacturing, telecommunications, chemical services, technology, public sector, and utilities. Stefanini is a CMM level 5, IT consulting company with a global presence. We are a CMM Level 5 company.