Data Scientist (Face to Face Interview - Locals Preferred)

Data Scientist

Auburn Hills, MI

Long Term Contract

Job Description:

Senior Data Scientist responsible for designing, building, deploying, and operationalizing

ML, Generative AI, LLM, Agentic AI, and RAG solutions

, primarily on AWS, with strong engineering, governance, security, monitoring, and model-quality practices

Experience:

8+ Years Data Science

Core Skills:

• AI / ML:

Machine Learning, Generative AI, LLMs, OpenAI APIs, Agentic AI, Multi-Agent Orchestration, Tool Calling, RAG, Semantic RAG, Embeddings, Vector Databases, Semantic Retrieval

• Programming / Frameworks:

Python, Java, PyTorch, TensorFlow, Hugging Face, LangChain, LlamaIndex

• AWS / Cloud:

AWS, SageMaker, Lambda, S3, ECS/EKS, Bedrock, GPU Compute; GCP/Azure

• Engineering:

Docker/Containers, CI/CD, ML Pipelines, Infrastructure as Code, DevSecOps, Security Scanning, Secrets Management

• Frontend:

React, TypeScript, Java-based UI

• MLOps / Quality:

Model Monitoring, Drift Detection, Performance Tracking, Accuracy, Precision/Recall, Latency, Cost, Hallucination/Bias Metrics, Automated Testing, Model Versioning, Retraining/Fine-Tuning

• Governance / Security:

AI Governance, Responsible AI, Guardrails, Data Privacy, Bias Mitigation, Content Safety, Access Control, Prompt-Injection Defense, Compliance, Audit/Lineage

Key Responsibilities:

• Build, train, validate, deploy, and operationally support ML/AI models and pipelines.
• Develop

LLM, Generative AI, Agentic AI, and semantic RAG

solutions using appropriate models, tools, orchestration, and retrieval strategies.

• Determine when to use AI/LLM versus traditional statistical or deterministic approaches.
• Build HITL/HOTL workflows for model oversight, approval, retraining, and tuning.
• Deploy primarily on AWS using SageMaker, Bedrock, Lambda, S3, ECS/EKS, and GPU infrastructure.
• Develop Python/Java model services and interactive React/TypeScript applications for model outputs.
• Implement Docker, CI/CD, DevSecOps, security scanning, IaC, and automated compliance.
• Monitor production models for performance, drift, bias, latency, cost, and reliability.
• Manage model versioning, retraining, fine-tuning, validation, logging, audit trails, and lineage.
• Define measurable model-quality criteria and automated test coverage.
• Implement AI governance, security, privacy, responsible-AI controls, and LLM/agentic guardrails.

Preferred / Good-to-Have:

• GCP and Azure ML/AI services.
• Responsible AI, fairness/bias testing, and model explainability.
• AWS ML/AI or relevant cloud certifications.