Technology Architect | Cloud Platform | Google Cloud - Architecture

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

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

• GCP
• Prompt Engineering

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.

• 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.

• 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 Lang Chain, Lang Graph, and agentic AI patterns including routing, memory, multi‑agent orchestration, guardrails, and failure recovery.
• Experience in cloud‑native engineering across AWS (Sage Maker, 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, Tensor Flow).
• 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 Sage Maker, 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, Sage Maker Pipelines, Vertex AI Pipelines, and Git Hub Actions.
• Experience using cloud‑native AI systems on AWS (Sage Maker, 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 Lang Graph and CrewAI for tool‑calling, validation agents, automated reasoning, and workflow supervision.

10+ years

Face to face interview