Job Role: Gen-AI Engineer
Work Location: Charlotte, NC 28202
Work Model : -Hybrid. LOCAL CANDIDATES ONLY!!!
Interview Process: Face to face interview
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