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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.
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:
Google Cloud Platform
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 Google Cloud Platform 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 Google Cloud Platform (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, Google Cloud Platform 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 Google Cloud Platform 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
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