What are the responsibilities and job description for the Generative AI Architect position at Thinkbyte Consulting Inc. ( E-Verified )?
- GenAI Architect Location: United States (Preferred: Bay Area, CA)
We are looking for a Generative AI Architect to bridge the gap between business and engineering teams, transforming requirements into scalable AI architectures (not development). The ideal candidate will have hands-on experience designing and implementing Generative AI solutions, including Retrieval-Augmented Generation (RAG), fine-tuning large models, and scalable architectures.
Key Responsibilities
- Collaborate with business stakeholders and engineering teams to translate AI requirements into scalable architectures.
- Design and implement Generative AI solutions, including LLMs, RAG pipelines, and fine-tuned models.
- Develop strategies for model training, inference, deployment, and optimization in production environments.
- Utilize ML frameworks (TensorFlow, PyTorch) to build and optimize generative models.
- Work with AWS AI/ML services, containerization technologies (Docker, Kubernetes), and cloud-based deployment strategies.
- Implement and optimize Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer models (GPT, BERT, etc.) for different use cases.
- Handle large datasets for data cleaning, augmentation, and preprocessing to improve AI model performance.
- Ensure AI solutions adhere to best practices for scalability, security, and compliance in cloud environments.
- Experience: Proven experience in Generative AI model development, architecture, and deployment.
- Programming Skills: Strong proficiency in Python and ML frameworks like TensorFlow or PyTorch.
- Cloud & DevOps: Hands-on experience with AWS Generative AI services, containerization (Docker, Kubernetes), and cloud-based AI deployments.
- Generative AI Expertise: Deep understanding of RAG, model fine-tuning, GANs, VAEs, and Transformer-based models.
- Data Handling: Ability to work with large datasets, perform data cleaning, augmentation, and analysis.
- Educational Background: Bachelor's or Master's in Computer Science, Machine Learning, AI, or a related field.
- Experience working with MLOps pipelines and deploying models in production.
- Familiarity with vector databases (e.g., Pinecone, FAISS) and prompt engineering techniques.
- Previous experience in designing GenAI solutions for enterprise applications.
