Develop neural networks and transformer architectures for NLP and computer vision. Explore and experiment with multi-modal diffusion architectures as a good-to-have.
LLM Development & Implementation:
Build, fine-tune, and deploy large language models (LLMs) for document comparison, chunking, RAG, chatbot, and NLP applications. Integrate embedding models with vector databases such as FAISS, Pinecone, and AWS OpenSearch.
Multi-Agent & Rules-Based AI Implementation:
Implement rules-based AI logic in applications. Develop and orchestrate multi-agent AI systems. Use frameworks like LangChain, LangGraph, and LlamaIndex (GPT Index) to chain multiple LLM workflows.
AI Tools & Libraries:
Utilize AWS AI services including SageMaker and Amazon Bedrock. Work with Hugging Face, OpenAI API, and GPT models to enhance AI solutions.
Cloud & DevOps (AWS Focused):
Deploy AI models using AWS Fargate, EKS, ECS. Implement CI/CD pipelines using AWS CodeBuild, CodePipeline, and Lambda Functions. Containerize AI services with Docker and Kubernetes.
Model Deployment & API Integration:
Deliver AI projects from inception to production using Python. Develop and integrate APIs (REST, GraphQL, AWS API Gateway). Reuse and customize GitHub-based AI repositories for production readiness.
Collaboration & Communication:
Work with cross-functional teams to understand business requirements and communicate AI concepts to non-technical stakeholders.
Research & Innovation:
Stay updated on AI trends, advanced architectures, and emerging technologies. Conduct research to explore innovative AI methodologies.
Documentation & Reporting:
Document AI models, workflows, and processes. Prepare reports and presentations on project progress, results, and business impact.
