GenAI Architect
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a GenAI Architect with a contract length of "unknown," offering a pay rate of "unknown." Key skills include expertise in LLMs, model tuning, Python, and data engineering workflows. Experience with AI ethics and generative models is required.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
-
🗓️ - Date discovered
May 30, 2025
🕒 - Project duration
Unknown
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🏝️ - Location type
Unknown
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📄 - Contract type
Unknown
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🔒 - Security clearance
Unknown
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📍 - Location detailed
United States
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🧠 - Skills detailed
#NumPy #SQL (Structured Query Language) #Spark (Apache Spark) #API (Application Programming Interface) #ML (Machine Learning) #Reinforcement Learning #Langchain #Scala #Data Engineering #Pandas #Python #Generative Models #Monitoring #Deployment #AI (Artificial Intelligence) #PyTorch #Transformers #"ETL (Extract #Transform #Load)" #Data Science #Unsupervised Learning #Programming #Airflow #Databases #Supervised Learning #TensorFlow #Conda
Role description
Primary Skills: (AI Data Scientist)
• Deep Expertise in the following:
• Large Language Models (LLMs) and Multimodels
• Foundation Model Architectures (Transformers, Encoder/Decoder)
• API integration from providers like (e.g., OpenAI, Cohere).
• Model tuning pipeline development
• Prompt engineering
• Reinforcement learning from human feedback (RLHF).
• Frameworks : PyTorch, TensorFlow,LangChain, LlamaIndex
• Strong programming skills in Python, and experience with data engineering workflows (Spark, Airflow, SQL).
• Hands on experience in
• Fine-tuning & Prompt Engineering (LoRA, PEFT)
• Retrieval-Augmented Generation (RAG) pipelines
• Python: NumPy, Pandas, Scikit-learn, HuggingFace, OpenAI API
• Knowledge of AI ethics, explainability, and governance in generative models
Secondary skills:
• Experience with vector databases (e.g., FAISS, Weaviate, Pinecone) and scalable RAG systems
• Familiarity with GPU compute infrastructure and distributed model training
• MLOps for LLMs: Deployment, Monitoring, Versioning
• Classical Machine learning like Supervised Learning, UnSupervised learning models
Primary Skills: (AI Data Scientist)
• Deep Expertise in the following:
• Large Language Models (LLMs) and Multimodels
• Foundation Model Architectures (Transformers, Encoder/Decoder)
• API integration from providers like (e.g., OpenAI, Cohere).
• Model tuning pipeline development
• Prompt engineering
• Reinforcement learning from human feedback (RLHF).
• Frameworks : PyTorch, TensorFlow,LangChain, LlamaIndex
• Strong programming skills in Python, and experience with data engineering workflows (Spark, Airflow, SQL).
• Hands on experience in
• Fine-tuning & Prompt Engineering (LoRA, PEFT)
• Retrieval-Augmented Generation (RAG) pipelines
• Python: NumPy, Pandas, Scikit-learn, HuggingFace, OpenAI API
• Knowledge of AI ethics, explainability, and governance in generative models
Secondary skills:
• Experience with vector databases (e.g., FAISS, Weaviate, Pinecone) and scalable RAG systems
• Familiarity with GPU compute infrastructure and distributed model training
• MLOps for LLMs: Deployment, Monitoring, Versioning
• Classical Machine learning like Supervised Learning, UnSupervised learning models
