Technology Architect | Cloud Platform | Google Cloud - Architecture

Expedite Talent Solutions; Charlotte (NC); Hybrid; USD 150,000 - 210,000; Full time

Job summary

Expedite Talent Solutions in Charlotte, NC seeks a senior Generative AI Engineer to design, develop, and deploy cutting-edge AI solutions. You will leverage LLMs, prompt engineering, and Gen AI frameworks to build scalable applications and agentic AI systems.

The role demands 10+ years in AI, strong Python, and cloud experience across AWS/GCP/Azure with a focus on MLOps and end-to-end LLMOps pipelines. Hybrid onsite work available in the Charlotte area.

Qualifications

• Hands-on Gen AI with Python experience and LLMs.
• Expertise in retrieval augmented generation and RAG pipelines.
• Experience with multi-agent patterns and memory management.

Responsibilities

• Design and deploy Generative AI models for text, images, or multimodal data.
• Develop prompt strategies and embedding-based retrieval systems.
• Integrate Gen AI into web apps and enterprise workflows.
• Build agentic AI apps with context engineering and MCP tools.

Skills

Gen AI

Agentic AI

VLLM

Fast API

REST API

Lang Graph

Lang Chain

Graph RAG

ML Ops

Python

ML

Data Science

RAG

LLM

Tools

Docker

Kubernetes

FastAPI

REST APIs

Pydantic/JSON schemas

LangChain

LangGraph

Pinecone

FAISS

ChromaDB

Weaviate

Hugging Face

PyTorch

TensorFlow

MLflow

DVC

Vertex AI

SageMaker

Job description

Job Title: Technology Architect | Cloud Platform | Google Cloud - Architecture – Gen AI Engineer

Work Location & Reporting Address: Charlotte, NC 28202 (Onsite-Hybrid. LOCAL CANDIDATES ONLY!!!)

Contract Duration: 12 months

Maximum Vendor Rate: * per hour max

Target Start Date: 01 Jul 2026

Visa Independence: Yes

Must Have Skills

• GEN AI
• Agentic AI
• VLLM
• Fast API
• REST API
• MCD
• Lang Graph
• Lang Chain
• Graph RAG
• ML Ops
• Python
• ML
• Data Science
• RAG
• LLM

Nice to Have Skills

• GCP
• Prompt Engineering

Detailed Job Description

We are seeking a highly skilled Generative AI Engineer with a strong Python background to design, develop, and deploy cutting‑edge AI solutions. The ideal candidate will have hands‑on experience with Large Language Models (LLMs), prompt engineering, and Gen AI frameworks, alongside expertise in building scalable AI applications and developing Agentic AI solutions.

Key Responsibilities

• Design and implement Generative AI models for text, image, or multimodal applications.
• Develop prompt engineering strategies and embedding‑based retrieval systems.
• Integrate Gen AI capabilities into web applications and enterprise workflows.
• Build agentic AI applications with context engineering and MCP tools.

Required Skills & Qualifications

• 7+ years of hands‑on experience in AI, Data Science, ML, and Gen AI.
• 2+ years of strong hands‑on experience in Agentic AI, VLLM’s, Gen AI, Lang Chain, Lang Graph, RAG, LLM Oops, and AI Services in GCP and Azure.
• Strong experience designing and deploying Retrieval‑Augmented Generation (RAG) pipelines.
• Strong MLOps/LLMOps experience with CI/CD automation.
• Extensive experience with LangChain, LangGraph, and agentic AI patterns including routing, memory, multi‑agent orchestration, guardrails, and failure recovery.
• Experience in cloud‑native engineering across AWS (SageMaker, Lambda, ECS/Fargate, S3, API Gateway, Step Functions) and GCP (Vertex AI) for scalable AI delivery.
• Experience developing microservices and API development using FastAPI, REST APIs, Pydantic/JSON schemas, Docker, and Kubernetes for low‑latency serving.
• Strong experience with vector databases and semantic search technologies including Pinecone, FAISS, ChromaDB, and embedding lifecycle management.
• Strong proficiency in Python and AI/ML frameworks (PyTorch, TensorFlow).
• Hands‑on experience using session and memory for building multi‑agent systems along with using MCP tools.
• Hands‑on experience with LLMs, transformers, and Hugging Face ecosystem.
• Knowledge and experience with vector databases and RAG technique for semantic search.
• Familiarity with cloud AI services (AWS SageMaker, Azure OpenAI, GCP Vertex AI).
• Understanding of MLOps practices for scalable AI deployment.
• Strong experience in working with LLM fine‑tuning with LoRA, QLoRA, PEFT.
• Strong experience designing advanced RAG systems using Pinecone, FAISS, Weaviate, Chroma, hybrid retrieval, and custom embeddings.
• Strong experience in designing end‑to‑end LLMOps/MLOps pipelines using MLflow, DVC, SageMaker Pipelines, Vertex AI Pipelines, and GitHub Actions.
• Experience using cloud‑native AI systems on AWS (SageMaker, Lambda, EKS, EC2, Step Functions, S3, Glue) and GCP Vertex AI, supporting high‑volume inference and secure enterprise operations.
• Experience developing multi‑agent orchestration workflows using LangGraph and CrewAI for tool‑calling, validation agents, automated reasoning, and workflow supervision.

Minimum Years of Experience

10+ years

Interview Process

Face to face interview