Data Scientist

VBeyond Corporation; Auburn Hills (MI); On-site; USD 120,000 - 160,000; Full time

Job summary

VBeyond Corporation is seeking a skilled ML/AI engineer to advance production models in a fast-paced environment in Auburn Hills, MI. You will build agentic AI solutions, integrate LLMs, and oversee RAG pipelines while ensuring governance, security, and testing excellence.

The role requires hands-on Python/Java, containerized workflows with Docker, and strong front-end surfacing of model outputs via React/TypeScript/Java UI, with CI/CD and HITL considerations.

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).

Skills

Python

Java

LLMs

Agentic AI

RAG systems

Data quality

Feature engineering

React

TypeScript

Java UI

Docker

GPUs

CI/CD

DevSecOps

Monitoring

Governance

Human-in-the-loop

Testing strategies

Security compliance

ML frameworks

PyTorch

TensorFlow

Hugging Face

LangChain

LlamaIndex

Tools

Python

Java

React

TypeScript

Docker

GPU compute

CI/CD

DevSecOps

PyTorch

TensorFlow

Hugging Face

LangChain

LlamaIndex

Job description

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).

Best Regards,

Akhilesh Gupta

Note – VBeyond is fully committed to Diversity and Equal Employment Opportunity.