Data Scientist

VDart Inc; Auburn Hills (MI); On-site; USD 90,000 - 130,000; Part time

Job summary

VDart Inc. is seeking a Data Scientist to design, build, and operate ML/AI solutions on AWS, with a strong focus on LLMs, agentic AI, and RAG architectures. The role emphasizes governance, security, and end-to-end model lifecycle management in an onsite contract setting.

The candidate should have hands-on experience with Python/Java, containerization, and front-end delivery of model outputs, plus CI/CD and DevSecOps practices across ML workflows.

Qualifications

• Experience building ML solutions on AWS and deploying them to production.
• Hands-on expertise with LLMs and OpenAI APIs; knowledge of leading AI/LLM families.
• Proven ability to design agentic AI systems (multi-agent orchestration, tool use, autonomous workflows).

Responsibilities

• Design, build, train, and validate ML models with strong data understanding.
• Develop solutions using LLMs and generative AI, staying current with model releases.
• Build agentic AI solutions (multi-step, tool-using, autonomous agents).
• Create RAG architectures with embeddings and vector search; evaluate retrieval strategies.
• Implement HITL/HOTL patterns and governance in inference and retraining workflows.
• Provide operational support, monitoring, and incident triage for ML/AI services.
• Own model update lifecycle: retraining, fine-tuning, versioning, re-validation.

Skills

AWS

LLMs

Agentic AI

RAG

Python

Java

React/TypeScript UI

Docker

CI/CD

DevSecOps

Governance & Monitoring

HITL / HOTL

Testing & QA

Model Governance

Tools

SageMaker

OpenAI APIs

Vector Databases

Docker

CI/CD pipelines

PyTorch

TensorFlow

Hugging Face

LangChain

Job description

Role: Data Scientist

Location: Auburn Hills, MI (Onsite)

Type: Contract

Position Summary

• We are seeking a highly technical Data Scientist with deep cloud experience, primarily AWS, to design, build, and operationalize machine learning and AI/LLM solutions.
• This role requires strong engineering discipline (containers, CI/CD, DevSecOps), current knowledge of generative and agentic AI, front-end delivery of model outputs, and a rigorous approach to governance, security, monitoring, and measurable model quality across the full model lifecycle.

Key Responsibilities:

Model Development & AI/ML Engineering

• Design, build, train, and validate machine learning models with deep understanding of the underlying data, feature engineering, and model behavior.
• Develop solutions using LLMs and generative AI, including OpenAI modules/APIs, staying current with the latest AI/LLM model releases and capabilities.
• Design and implement agentic AI solutions (multi-step, tool-using, autonomous/semi-autonomous agents), understanding orchestration, memory, and tool-calling patterns.
• Build and evaluate RAG (Retrieval-Augmented Generation) solutions, including semantic RAG architectures (embeddings, vector search, semantic chunking/retrieval strategies).
• Exercise sound judgment on when to apply AI/LLM solutions vs. traditional deterministic or statistical approaches, and select the appropriate model type/size/architecture for a given problem.
• Design for human-in-the-loop (HITL) and human-on-the-loop (HOTL) patterns appropriately, determining where human review, approval, or oversight is required in inference, retraining, or tuning workflows.
• Establish clear, measurable testing and evaluation criteria for model builds (accuracy, precision/recall, drift, latency, cost, hallucination rate, bias metrics).
• Write and maintain automated test cases for model validation, including using AI-assisted tools to generate and expand test coverage for model builds.
• Operational Support & Model Lifecycle
• Provide operational support for deployed models, including monitoring, incident triage, and troubleshooting of production ML/AI services.
• Implement governance and monitoring frameworks around deployed models to track performance, drift, bias, and usage over time.
• Own the model update lifecycle: retraining, fine-tuning, versioning, and periodic re-validation as data and business conditions evolve.
• Use logging/chronicle-based tracing and audit trails to track model decisions, retraining events, and lineage over time.

Cloud, Engineering & Front-End

• Build and deploy models and pipelines primarily on AWS (e.g., SageMaker, Lambda, S3, ECS/EKS, Bedrock); working knowledge of GCP and Azure is a plus.
• Strong coding skills in Python and Java for model services, pipelines, and backend integration.
• Build interactive front-end UI applications to present model outputs and insights using React, TypeScript, or Java-based frameworks.
• Containerize model workloads using Docker/containers, and manage GPU-based compute for training and inference workloads.
• Build and maintain CI/CD pipelines for model training, validation, and deployment.
• Apply DevSecOps principles across the ML lifecycle: security scanning, secrets management, infrastructure as code, and automated compliance checks.
• Governance, Security & Responsible AI
• Maintain deep awareness of governance, legal, and security requirements applicable to AI/ML model development and data usage.
• Design and implement guardrails in model development (data privacy, bias mitigation, content safety, access controls, prompt injection defenses for LLM/agentic systems).
• Ensure models and pipelines meet organizational and regulatory compliance requirements prior to production release.

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.