Responsibilities
- Integrate large language models and generative AI technologies into web platforms using well-designed APIs.
- Create optimized API endpoints that enable real-time communication between front-end interfaces and back-end AI systems.
- Develop secure, scalable microservices architecture tailored for AI model deployment.
- Build high-performance back-end services in Python to support generative AI model operations.
- Construct and manage data processing pipelines for input preparation and model output interpretation.
- Apply data security standards to protect sensitive information and sustain model accuracy.
- Use Docker and Kubernetes for containerizing and managing AI application deployments.
- Set up automated CI/CD pipelines for efficient code integration, testing, and deployment.
- Maintain reliable cloud infrastructure on platforms like Google Cloud Platform or Azure for training, storing, and deploying models.
- Leverage vector databases such as Pinecone, Weaviate, or Faiss for embedding storage and similarity-based retrieval.
- Work with AI frameworks including Hugging Face Transformers, LangChain, and OpenAI tools.
- Fine-tune and optimize LLMs to meet specific performance requirements of applications.
