Technology Architect | Cloud Platform | Google Cloud - Architecture

POC: Sam Chavez

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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

MAX VENDOR RATE: XXX per hour max

Target Start Date: 01 Jul 2026

Does this position require Visa independent candidates only? 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, along with expertise in building scalable AI applications. Experience in 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, GEN AI

• 2 years of strong hands on experience in Agentic AI, VLLM's, GEN AI, Lang Chain, Lang Graph, RAG, LLM OPS and AI Services in GCP and Azure.

• Strong hands on 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 in Developing microservices and API development using FastAPI, REST APIs, Pydantic/JSON schemas, Docker, and Kubernetes for low-latency serving.

• Strong Hands-on 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 in Architected 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 in 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 in 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

Certifications Needed:

Top 3 responsibilities you would expect the Subcon to shoulder and execute:

• Strong experience in GEN AI, LLM, RAG,ML, DL,ML Ops, LLMOps, Cloud platform,Model servicing optimization, Python

• Strong communication skills

• Strong programming skills

Interview Process (Is face to face required?)

• Face to face interview

Any additional information you would like to share about the project specs/nature of work:

Project Code: of Observability, Agentic AI Use cases f