Red Oak Technologies
AI/ML [NLP GenAI RAG LangChain PyTorch) On-Site Contract at Sunnyvale California
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is an AI/ML contract position in Sunnyvale, California, focusing on Generative AI solutions. Key skills include Python, PyTorch, Java, and experience with cloud platforms. Candidates should have expertise in NLP, containerization, and distributed computing. Contract length and pay rate are unspecified.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
520
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🗓️ - Date
May 8, 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
Sunnyvale, CA
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🧠 - Skills detailed
#Distributed Computing #Hugging Face #AWS (Amazon Web Services) #GCP (Google Cloud Platform) #PyTorch #TensorFlow #Deployment #AI (Artificial Intelligence) #Java #NLP (Natural Language Processing) #Scala #Kubernetes #Langchain #Docker #Keras #Cloud #ML (Machine Learning) #Python
Role description
AI/ML [NLP GenAI RAG LangChain PyTorch) On-Site Contract at Sunnyvale California
- design, development, and deployment of AI/ML
- Generative AI solutions, such as NLP-based
- deploy machine learning models, ensuring scalability, performance, and maintainability.
- Java experience and integrating AI/ML models into production
- Generative AI (RAG pipelines, LLMs, fine-tuning, and prompt engineering).
- Python and using ML frameworks like PyTorch, TensorFlow/Keras, and Hugging Face.
cloud platforms, such as GCP or AWS, and distributed computing environments.
containerization tools like Docker and orchestration frameworks like Kubernetes.
LangChain, OpenAI APIs, or other Generative AI platforms
Go or Scala.
ML pipelines and frameworks like Kubeflow
multimodal machine learning techniques.
AI/ML [NLP GenAI RAG LangChain PyTorch) On-Site Contract at Sunnyvale California
- design, development, and deployment of AI/ML
- Generative AI solutions, such as NLP-based
- deploy machine learning models, ensuring scalability, performance, and maintainability.
- Java experience and integrating AI/ML models into production
- Generative AI (RAG pipelines, LLMs, fine-tuning, and prompt engineering).
- Python and using ML frameworks like PyTorch, TensorFlow/Keras, and Hugging Face.
cloud platforms, such as GCP or AWS, and distributed computing environments.
containerization tools like Docker and orchestration frameworks like Kubernetes.
LangChain, OpenAI APIs, or other Generative AI platforms
Go or Scala.
ML pipelines and frameworks like Kubeflow
multimodal machine learning techniques.
