Generative AI Engineer

Job Title: Senior Generative AI Engineer with a strong Python Work Location: Charlotte, NC, 28202 (please submit profiles within this area only) Contract duration: 12 In person Interview - Mandatory Job Details: Must Have Skills: GEN AI, Agentic AI Cortex AI,, 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: 10+ years of hands-on experience in AI, Data science, ML, GEN AI. Strong hands on experience designing and deploying Retrieval-Augmented Generation (RAG) pipelines Strong hands‑on experience with RAG pipelines and vector databases Extensive experience with LangChain, LangGraph, CrewAI, multi‑agent orchestration Strong MLOps / LLMOps experience with CI/CD automation Experience across AWS (SageMaker, Lambda, EKS, S3) and GCP (Vertex AI) API & microservices development using FastAPI, REST, Docker, Kubernetes Strong Python proficiency with PyTorch / TensorFlow 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 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.