Artificial Intelligence Engineer

⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for an Artificial Intelligence Engineer in Alpharetta, GA, on a 6-month contract. Required skills include Python, AI frameworks (LangChain, LangGraph), and cloud platforms (AWS/GCP). USC/GC candidates only; face-to-face interview required.
🌎 - Country
United States
πŸ’± - Currency
$ USD
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πŸ’° - Day rate
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πŸ—“οΈ - Date discovered
August 23, 2025
πŸ•’ - Project duration
More than 6 months
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🏝️ - Location type
Hybrid
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πŸ“„ - Contract type
Fixed Term
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πŸ”’ - Security clearance
Unknown
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πŸ“ - Location detailed
Atlanta, GA
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🧠 - Skills detailed
#BitBucket #Version Control #AI (Artificial Intelligence) #Data Science #Datasets #Langchain #NLP (Natural Language Processing) #Programming #Kubernetes #Cloud #PyTorch #TensorFlow #Libraries #AWS (Amazon Web Services) #GCP (Google Cloud Platform) #Python #Databases #GIT #ML (Machine Learning) #Docker
Role description
AI Engineer at Atlanta GA Location: Alpharetta, GA (Hybrid) Face-to-Face Interview Required) Duration: 6-month contract with potential extension or full-time conversion USC/GC ONLY Objective: Design and deploy advanced multi-agent systems leveraging enterprise and external datasets to deliver impactful AI solutions to millions of users. Key Responsibilities: β€’ Architect AI agent systems using frameworks such as LangChain, LangGraph, and CrewAI. β€’ Develop workflows for decision-making, reasoning, and adaptive behaviors using Python. β€’ Integrate Large Language Models (LLMs) with traditional pipelines for hybrid intelligence solutions. β€’ Design orchestration systems for task allocation and interaction management. Required Skills: β€’ Proficiency in Python programming. β€’ Hands-on experience in Agentic Application Development and Prompt Engineering. β€’ Expertise in AI agent frameworks (e.g., LangChain, LangGraph). β€’ Familiarity with cloud platforms (AWS/GCP) and version control tools (Git/Bitbucket). Preferred Qualifications: β€’ Knowledge of vector databases, Retrieval-Augmented Generation (RAG), ML libraries (PyTorch, TensorFlow), Docker/Kubernetes, and machine learning algorithms. β€’ Background in Data Science, Natural Language Processing (NLP), or Knowledge Engineering.