Intone Networks Inc
AI Engineer – GenAI / Agentic Systems
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for an AI Engineer – GenAI / Agentic Systems, based in Charlotte, NC or Dallas, TX, with a contract length of unspecified duration and a pay rate of "unknown." Requires 5+ years of AI/ML experience, strong Python skills, and familiarity with cloud platforms.
🌎 - Country
United States
💱 - Currency
Unknown
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💰 - Day rate
Unknown
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🗓️ - Date
March 26, 2026
🕒 - Duration
Unknown
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🏝️ - Location
On-site
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📄 - Contract
Unknown
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🔒 - Security
Unknown
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📍 - Location detailed
Charlotte, NC
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🧠 - Skills detailed
#Scala #REST (Representational State Transfer) #Model Evaluation #Computer Science #Cloud #Monitoring #Docker #GCP (Google Cloud Platform) #FastAPI #Knowledge Graph #ML (Machine Learning) #Python #REST API #AWS (Amazon Web Services) #"ETL (Extract #Transform #Load)" #Automated Testing #AI (Artificial Intelligence) #Azure
Role description
Location: Charlotte, NC or Dallas, TX ( Local) Schedule: On-site 5 days/week Need some one who can attend In person interview What You’ll Do Build GenAI applications leveraging foundation models and advanced architectures such as GraphRAG. Develop autonomous AI agents using modern agentic frameworks. Design and deploy RAG and GenAI services using Python (FastAPI), Docker, and cloud platforms (AWS, Azure, or GCP). Build scalable REST APIs that power LLM-driven applications integrated with enterprise data sources. Implement LLM evaluation frameworks using tools such as Ragas, Lang Smith, or custom benchmarks to measure answer relevance, groundedness, and hallucination rates. Apply LLMOps/MLOps practices, including CI/CD pipelines, prompt/version management, automated testing, and monitoring of latency, cost, and response quality. Develop systems leveraging embeddings at scale, knowledge graphs, and ontology extraction. Collaborate across engineering teams, mentor developers, and help drive innovation in GraphRAG and agentic AI architectures. What You’ll Bring Degree in Computer Science, AI/ML, or related field. 5+ years of AI/ML-focused software engineering experience. Production experience building LLM-based or agentic AI systems. Strong expertise in Python and modern AI frameworks. Experience with embeddings, knowledge graphs, ontology extraction, and advanced RAG/GraphRAG implementations. Full-stack development experience (Python back end + modern front end). Experience deploying AI workloads to AWS, Azure, or GCP. Familiarity with LLMOps/MLOps tooling and model evaluation frameworks. Strong problem-solving, communication, and collaboration skills.
Location: Charlotte, NC or Dallas, TX ( Local) Schedule: On-site 5 days/week Need some one who can attend In person interview What You’ll Do Build GenAI applications leveraging foundation models and advanced architectures such as GraphRAG. Develop autonomous AI agents using modern agentic frameworks. Design and deploy RAG and GenAI services using Python (FastAPI), Docker, and cloud platforms (AWS, Azure, or GCP). Build scalable REST APIs that power LLM-driven applications integrated with enterprise data sources. Implement LLM evaluation frameworks using tools such as Ragas, Lang Smith, or custom benchmarks to measure answer relevance, groundedness, and hallucination rates. Apply LLMOps/MLOps practices, including CI/CD pipelines, prompt/version management, automated testing, and monitoring of latency, cost, and response quality. Develop systems leveraging embeddings at scale, knowledge graphs, and ontology extraction. Collaborate across engineering teams, mentor developers, and help drive innovation in GraphRAG and agentic AI architectures. What You’ll Bring Degree in Computer Science, AI/ML, or related field. 5+ years of AI/ML-focused software engineering experience. Production experience building LLM-based or agentic AI systems. Strong expertise in Python and modern AI frameworks. Experience with embeddings, knowledge graphs, ontology extraction, and advanced RAG/GraphRAG implementations. Full-stack development experience (Python back end + modern front end). Experience deploying AI workloads to AWS, Azure, or GCP. Familiarity with LLMOps/MLOps tooling and model evaluation frameworks. Strong problem-solving, communication, and collaboration skills.
