Strategic Staffing Solutions

Machine Learning Engineer

⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Machine Learning Engineer (Gen AI) in Charlotte, NC, hybrid onsite, for 12+ months at $78-80/hr W2. Requires 5+ years software engineering, 3+ years AI/ML experience, proficiency in Python/Java, and strong skills in RAG and LLM orchestration.
🌎 - Country
United States
πŸ’± - Currency
$ USD
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πŸ’° - Day rate
640
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πŸ—“οΈ - Date
April 28, 2026
πŸ•’ - Duration
More than 6 months
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🏝️ - Location
Hybrid
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πŸ“„ - Contract
W2 Contractor
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πŸ”’ - Security
Unknown
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πŸ“ - Location detailed
Charlotte Metro
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🧠 - Skills detailed
#Data Integration #AI (Artificial Intelligence) #ML (Machine Learning) #Deployment #Programming #API (Application Programming Interface) #Python #Java
Role description
Gen AI (AI/ML) Engineer Charlotte, NC – hybrid onsite 12+ months $78-80/hr W2 – NO 1099 NO CTC NO THIRD PARTY β€’ 5+ years of overall software engineering experience, or equivalent demonstrated through work experience, training, military experience, or education. β€’ 3+ years of hands-on experience building and deploying AI/ML or Generative AI solutions in production environments. Strong experience with: β€’ Retrieval-Augmented Generation (RAG) β€’ LLM orchestration and prompt engineering β€’ Agentic or workflow-based AI systems β€’ Proficiency in one or more programming languages such as Python, Java, or similar, with production-grade coding practices. β€’ Design, prototype, and deliver AI-driven workflows, agents, copilots, and automatons using large language models (LLMs) and enterprise AI services. β€’ Integrate AI capabilities with enterprise platforms and systems (e.g., ServiceNow, Salesforce, data platforms, internal services) using secure APIs and orchestrating patterns. Lead solution architecture and design activities, including: β€’ Prompt engineering and AI workflow design β€’ API integration and service orchestrating β€’ Enterprise knowledge and data integration β€’ Own solutions across the full lifecycleβ€”from concept and proof of value through production deployment and continuous improvement.