AI/ML Architect — AWS, LLMs, RAG & Governance

Apexon; Auburn Hills (MI); On-site; USD 120,000 - 190,000; Full time

Job summary

Apexon is seeking a highly technical Data Scientist with deep AWS experience to design, build, and operationalize ML and AI/LLM solutions. Core focus on governance, security, monitoring, and model quality across the lifecycle, with strong engineering discipline for containers, CI/CD, and DevSecOps.

The role emphasizes practical experience with AWS SageMaker, OpenAI APIs, agentic AI, RAG, and end-to-end model delivery including frontend UIs and scalable backends.

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.

Responsibilities

• Design, build, train, and validate ML models with strong data understanding and feature engineering.
• Develop LLM/generative AI solutions and stay current with model releases.
• Design agentic AI solutions (multi-step, tool-using, autonomous workflows).
• Build and evaluate RAG architectures including embeddings and semantic retrieval.
• Establish testing criteria for model quality and performance, including bias and drift metrics.
• Provide operational support for deployed models, including monitoring and incident response.

Skills

AWS

LLMs/API usage

Agentic AI

RAG systems

Python

Java

Frontend UI (React/TypeScript)

Docker/Containers

CI/CD

DevSecOps

Model governance & monitoring

AI testing & evaluation

ML model evaluation metrics

Tools

SageMaker

Lambda

S3

ECS/EKS

Bedrock

PyTorch/TensorFlow

LangChain/LlamaIndex

Vector databases

Job description

Apexon is seeking a highly technical Data Scientist with deep AWS experience to design, build, and operationalize ML and AI/LLM solutions. Core focus on governance, security, monitoring, and model quality across the lifecycle, with strong engineering discipline for containers, CI/CD, and DevSecOps.

The role emphasizes practical experience with AWS SageMaker, OpenAI APIs, agentic AI, RAG, and end-to-end model delivery including frontend UIs and scalable backends.