Job Description

Machine Learning Engineer (Generative AI)

Location: Charlotte, NC (Hybrid) 
Duration: 12 Month Contract
Employment Type: W2 Only

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 SageMaker
  • Azure OpenAI
  • 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 SageMaker
• Azure OpenAI
• Google Vertex AI
• MLOps
• REST APIs
• Cloud-Native Application Development