AI Integration Architect: RAG, LLMs & Microservices

TechDigital Group; Phoenix (AZ); On-site; USD 150,000 - 210,000; Full time

Job summary

TechDigital Group is seeking a seasoned Application/Solution Architect focused on AI/ML integration. You will design how AI models plug into core systems, build LLM pipelines with LangChain or similar tools, and work with vector databases to support RAG workflows.

You will evaluate platforms for cost, performance, and compliance, while ensuring data privacy and model risk is managed in production. Close collaboration with data science, engineering, and infrastructure teams is essential.

Qualifications

• 8+ years in application/solution architecture.
• 3+ years in AI/ML integration.
• Hands-on experience with LangChain, Hugging Face, TensorFlow or similar.
• Experience with LLM platforms: OpenAI, Anthropic, Azure OpenAI, AWS Bedrock.
• Strong grasp of RAG and vector databases (Pinecone, Weaviate, FAISS).
• APIs, microservices, event-driven architecture exposure.
• Proficient in AWS and programming in Python and Java.
• Understanding of MLOps/LLMOps.

Responsibilities

• Lead architecture for AI/ML integration across apps.
• Hands-on work with LangChain, LLM pipelines and vector DBs.
• Evaluate platforms for cost, performance, and compliance fit.
• Ensure data privacy, model risk and monitoring in production.
• Collaborate with data science, engineering and infrastructure teams.

Skills

LangChain

HuggingFace

TensorFlow

APIs & Microservices

Event-Driven Architecture

Python

Java

MLOps/LLMOps

OpenAI

Anthropic

Azure OpenAI

AWS Bedrock

Tools

Pinecone

Weaviate

FAISS

APIs

Docker

Job description

TechDigital Group is seeking a seasoned Application/Solution Architect focused on AI/ML integration. You will design how AI models plug into core systems, build LLM pipelines with LangChain or similar tools, and work with vector databases to support RAG workflows.

You will evaluate platforms for cost, performance, and compliance, while ensuring data privacy and model risk is managed in production. Close collaboration with data science, engineering, and infrastructure teams is essential.