Job Summary
Deep Learning & Transformer Architecture Develop a solid understanding of neural networks and their applications in various AI tasks. Design and implement transformer architectures for diverse applications, including natural language processing (NLP) and computer vision. Explore and experiment with multi-modal diffusion architectures as a good-to-have component. LLM Development & Implementation Build and fine-tune large language models (LLMs) for specific use cases, including document comparison, chunking, and retrieval-augmented generation (RAG). Develop and enhance chatbot and NLP-based applications to improve user interactions and experiences. Integrate embedding models with vector databases, utilizing tools such as FAISS, Pinecone, and AWS OpenSearch. Multi-Agent & Rules-Based AI Implementation Implement rules-based AI logic within applications to enhance decision-making processes. Develop and orchestrate multi-agent AI systems to solve complex problems efficiently. Utilize frameworks like LangChain, LangGraph, and LlamaIndex (GPT Index) to create and manage workflows that chain multiple LLMs together. AI Tools & Libraries Leverage AWS AI services, such as Amazon Bedrock and SageMaker, to build and deploy AI solutions. Utilize libraries and APIs from Hugging Face and OpenAI to enhance model capabilities and performance. Cloud & DevOps (AWS Focused) Deploy AI models using AWS services like Fargate, EKS, and ECS, ensuring scalability and reliability. Implement continuous integration and continuous deployment (CI/CD) pipelines for AI models using AWS CodeBuild, CodePipeline, and Lambda Functions. Work with containerized AI services using Docker and Kubernetes to streamline development and deployment processes. Model Deployment & API Integration Demonstrate hands-on Python experience by delivering at least two AI projects from inception to production. Develop and integrate APIs (REST APIs, GraphQL, AWS API Gateway) to facilitate communication between AI models and applications. Reuse and customize GitHub-based AI repositories, adapting them for production use and ensuring high-quality code standards.
Key Responsibilities
Qualifications & Competencies Required: Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field. 2+ experience in AI / MI roles with the required skills. Basics of Deep Learning Transformer Architecture Understanding of Neural Networks Transformer Architecture and Applications MultiModal Diffusion Architecture (Good to have) LLM Development & Implementation Hands-on experience in building