Machine Learning Engineer (Generative AI)

Machine Learning Engineer (Generative AI) Location: Charlotte, NC (Hybrid) Duration: 12 Month Contract Employment Type: W2 Only Pay: $70-84/hr W2 W2 ONLY, NO C2C Overview We are seeking a highly skilled Machine Learning Engineer specializing in Generative AI to design, develop, and deploy cutting-edge AI solutions that drive innovation across the enterprise. This role will focus on building scalable AI applications utilizing Large Language Models (LLMs), retrieval-augmented generation (RAG), agentic AI frameworks, and modern machine learning technologies. The ideal candidate combines strong software engineering fundamentals with hands-on experience developing and deploying production-grade AI solutions. This individual will partner closely with engineering teams, architects, and business stakeholders to build intelligent systems that solve complex business challenges and support enterprise-scale initiatives. Key Responsibilities • Design, develop, test, and deploy Generative AI solutions for text, image, and multimodal applications. • Build and optimize Large Language Model (LLM) applications using modern AI frameworks and tooling. • Develop advanced prompt engineering strategies and context-aware AI workflows. • Design and implement Retrieval-Augmented Generation (RAG) architectures utilizing vector databases and semantic search techniques. • Build agentic AI applications leveraging multi-agent frameworks, memory management, session handling, and Model Context Protocol (MCP) tools. • Integrate AI capabilities into enterprise applications, APIs, and business workflows. • Collaborate with cross-functional teams to define technical requirements and AI solution architecture. • Lead complex technology initiatives with enterprise-wide impact and influence AI engineering best practices. • Evaluate emerging AI technologies and recommend innovative solutions to improve business outcomes. • Develop scalable, secure, and maintainable AI applications following software engineering best practices. • Participate in code reviews, architecture discussions, testing, debugging, and technical documentation. • Mentor engineers and contribute to the development of AI engineering standards and best practices. • Support MLOps initiatives to ensure reliable deployment, monitoring, and lifecycle management of AI models. Required Qualifications • 5+ years of Software Engineering or Machine Learning Engineering experience, or equivalent combination of education, military experience, training, and professional experience. • Strong proficiency in Python development. • Experience with machine learning frameworks such as PyTorch and TensorFlow. • Hands-on experience building solutions with Large Language Models (LLMs), transformer architectures, and the Hugging Face ecosystem. • Experience developing multi-agent AI systems utilizing session management, memory frameworks, and MCP tools. • Knowledge of vector databases and Retrieval-Augmented Generation (RAG) architectures. • Experience building and deploying scalable AI applications in enterprise environments. • Strong understanding of software engineering principles, design patterns, and distributed systems. • Excellent problem-solving, communication, and collaboration skills. Preferred Qualifications • Experience with cloud-based AI platforms including: • AWS Sage Maker • Azure • Open AI Google Vertex AI • Experience implementing MLOps practices, model deployment pipelines, and AI lifecycle management. • Experience integrating AI solutions into web applications and enterprise platforms. • Familiarity with containerization technologies and cloud-native architectures. • Experience building multimodal AI applications. • Understanding of AI governance, security, and responsible AI practices. Desired Technical Skills • Python • PyTorch • TensorFlow • Hugging Face • Large Language Models • (LLMs)Prompt Engineering • Retrieval-Augmented Generation (RAG) • Vector Databases • Semantic Search Multi-Agent • Systems MCP (Model Context Protocol) • AWS Sage Maker • Open AI Google • Vertex AI • MLOps • REST APIs • Cloud-Native Application Development