Full Time On-Site: Data Scientist _ Auburn Hills, MI

ShineBask Technologies LLC; Auburn Hills (MI); Sur place; USD 110 000 - 160 000; Plein temps

Résumé du poste

ShineBask Technologies LLC in Auburn Hills, MI is seeking a Data Scientist to design, develop, and deploy ML solutions on AWS, incorporating LLMs and agentic AI workflows. The role emphasizes building RAG systems, semantic retrieval, and robust data quality practices.

The candidate will work with Python and Java, develop front-end interfaces, containerized GPU workloads, and CI/CD pipelines, ensuring governance, monitoring, and secure DevSecOps for production ML systems.

Qualifications

• 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).

Connaissances

ML on AWS

LLMs

OpenAI models

Agentic AI

RAG systems

Semantic RAG

Data quality

Feature engineering

Python

Java

React

TypeScript

UI in Java

Docker

GPU compute

ML CI/CD

DevSecOps

MLOps

Model governance

Human-in-the-loop

Model selection

Testing criteria

Automated tests

Guardrails

ML frameworks

PyTorch

TensorFlow

Hugging Face

LangChain

LlamaIndex

Outils

PyTorch

TensorFlow

Hugging Face

LangChain

LlamaIndex

Description du poste

Full Time On-Site: Data Scientist _ Auburn Hills, MI

Location: Auburn Hills, MI

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 GCP and Azure ML/AI services.
• Experience with responsible AI toolkits (bias/fairness testing, model explainability).
• Certifications in AWS ML/AI or relevant cloud platforms.