GenAI Architect

Overview

On Site
Depends on Experience
Accepts corp to corp applications
Contract - Independent
Contract - W2

Skills

Amazon Web Services
Cloud Computing
Data Integrity
Data Processing
Decision-making
Deep Learning
Machine Learning
Artificial Intelligence
GenAI
Generative Artificial Intelligence (AI)
TensorFlow
PyTorch
Machine Learning Operations (ML Ops)
Workflow
Python
Algorithms
Azure
GCP

Job Details

Dear applicants

Greetings from Vilwaa! At Vilwaa,1

We are focused towards delivering IT services and solutions in Data Engineering, Cloud Infrastructure solutions , Digital Application Development, (IoT) - Internet of Things.


Role: GenAI Architect
Location : San Jose, CA Job Description:

  • Implement and integrate Generative AI solutions into existing workflows.

  • Collaborate with cross-functional teams to ensure seamless deployment and scalability.

  • Develop, train, and fine-tune Generative AI models for various applications.

  • Optimize model performance using industry best practices and cutting-edge frameworks.

  • Implement Agentic AI techniques to enable autonomous decision-making systems.

  • Ensure models adhere to ethical standards and regulatory requirements.

  • Work with AWS/Azure cloud environments to ensure scalable infrastructure for AI workloads.

  • Utilize ML Studio for model experimentation, training, and deployment.

  • Collaborate with data engineers and scientists to ensure efficient AI data processing.

  • Build and maintain datasets required for training generative models, ensuring data integrity and quality.

  • Proficiency in developing Generative AI solutions for internet software products.

  • Strong understanding of deep learning algorithms, especially in generative models like GANs, VAEs, Diffusion models, or large-scale language models like GPT.

  • Hands-on experience with machine learning frameworks and libraries such as TensorFlow, PyTorch, or Hugging Face.

  • Strong programming skills in Python, including deep learning libraries (e.g., TensorFlow, Keras, PyTorch).

  • Familiarity with cloud platforms (AWS, Google Cloud Platform, or Azure) for model training and deployment.

  • Experience in large-scale model training, fine-tuning, or distributed computing.

  • Knowledge of MLOps practices for deploying, monitoring, and scaling AI models on cloud infrastructure.

  • Familiarity with AI ethics, bias mitigation, and interpretability in generative models.

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