AI/ML Engineer

Job Title: GEN AI/ML Engineer Location&: Dallas, TX or Charlotte, NC (Onsite-Hybrid. Will consider candidates willing to relocate to client’s location) Duration: 12 Monthts Must Have Skills: ⦁ GEN AI ⦁ Agentic 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 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