Data Scientist Auburn Hills, MI this role has Berribot test Required Skills & Qualifications (Mandatory) Strong hands-on experience building and deploying ML solutions on AWS. Proven experience with LLMs, including OpenAI models/APIs, and current knowledge of leading AI/LLM model families. Hands-on experience building agentic AI systems (multi-agent orchestration, tool use, autonomous workflows). Experience building RAG systems, including semantic RAG (embeddings, vector databases, semantic retrieval).

Deep understanding of data:

exploration, quality, feature engineering, and its impact on model outcomes. Strong coding proficiency in Python and Java. Experience building front-end interactive applications (React, TypeScript, or Java-based UI) to surface model outputs to end users. Practical experience with Docker/containers and GPU compute for training/inference. Experience building and maintaining CI/CD pipelines for ML/AI workloads. Working knowledge of DevSecOps practices applied to ML pipelines. Experience providing operational support for production ML/AI systems, including monitoring and incident response. Experience implementing model governance and monitoring (drift detection, performance tracking, periodic retraining/tuning cycles). Demonstrated ability to design for human-in-the-loop / human-on-the-loop workflows for model oversight, retraining, and tuning. Demonstrated judgment in model/technique selection, including when to use AI/LLM approaches vs. traditional methods. Experience defining measurable testing/evaluation criteria for model performance and quality. Experience writing automated test cases, including using AI-assisted approaches to generate test coverage for model builds. Solid understanding of AI governance, legal, and security requirements, and experience embedding guardrails into model development. Familiarity with ML/AI frameworks (e.g., PyTorch, TensorFlow, Hugging Face, LangChain/LlamaIndex or similar agentic/RAG frameworks). Preferred Qualifications Working knowledge of Google Cloud Platform and Azure ML/AI services. Experience with responsible AI toolkits (bias/fairness testing, model explainability). Certifications in AWS ML/AI or relevant cloud platforms. Data Scientist - this role has Berribot test Required Skills & Qualifications (Mandatory) Strong hands-on experience building and deploying ML solutions on AWS. Proven experience with LLMs, including OpenAI models/APIs, and current knowledge of leading AI/LLM model families. Hands-on experience building agentic AI systems (multi-agent orchestration, tool use, autonomous workflows). Experience building RAG systems, including semantic RAG (embeddings, vector databases, semantic retrieval).

Deep understanding of data:

exploration, quality, feature engineering, and its impact on model outcomes. Strong coding proficiency in Python and Java. Experience building front-end interactive applications (React, TypeScript, or Java-based UI) to surface model outputs to end users. Practical experience with Docker/containers and GPU compute for training/inference. Experience building and maintaining CI/CD pipelines for ML/AI workloads. Working knowledge of DevSecOps practices applied to ML pipelines. Experience providing operational support for production ML/AI systems, including monitoring and incident response. Experience implementing model governance and monitoring (drift detection, performance tracking, periodic retraining/tuning cycles). Demonstrated ability to design for human-in-the-loop / human-on-the-loop workflows for model oversight, retraining, and tuning. Demonstrated judgment in model/technique selection, including when to use AI/LLM approaches vs. traditional methods. Experience defining measurable testing/evaluation criteria for model performance and quality. Experience writing automated test cases, including using AI-assisted approaches to generate test coverage for model builds. Solid understanding of AI governance, legal, and security requirements, and experience embedding guardrails into model development. Familiarity with ML/AI frameworks (e.g., PyTorch, TensorFlow, Hugging Face, LangChain/LlamaIndex or similar agentic/RAG frameworks). Preferred Qualifications Working knowledge of Google Cloud Platform and Azure ML/AI services. Experience with responsible AI toolkits (bias/fairness testing, model explainability). Certifications in AWS ML/AI or relevant cloud platforms.