AI Solution Development: Design, develop, implement, and optimize machine learning models, algorithms, and AI systems for scalability and performance.
Data Management & Preprocessing: Collect, preprocess, and validate large datasets to ensure data quality and integrity for AI model development.
Model Training & Evaluation: Train, test, and validate AI models; perform evaluation and tuning to improve performance metrics.
Integration & Deployment: Integrate AI models into existing systems, deploy solutions into production, and monitor performance.
LLM Development & Implementation: Build and fine-tune large language models; implement document comparison, retrieval-augmented generation (RAG), chatbots, and NLP-based applications; integrate embeddings with vector databases such as FAISS, Pinecone, or AWS OpenSearch.
Multi-Agent & Rules-Based AI Implementation: Develop multi-agent AI systems; implement rules-based AI logic using tools like LangChain, LangGraph, and LlamaIndex.
AI Tools & Libraries: Utilize AWS AI services (SageMaker, Amazon Bedrock), Hugging Face, OpenAI API, and GPT models.
Cloud & DevOps: Deploy AI models on AWS (Fargate, EKS, ECS); implement CI/CD pipelines with AWS CodeBuild, CodePipeline, Lambda; containerize AI services using Docker and Kubernetes.
Model Deployment & API Integration: Develop and integrate APIs (REST, GraphQL, AWS API Gateway); customize GitHub-based AI repositories for production use.
Collaboration & Communication: Work closely with cross-functional teams and communicate AI concepts to non-technical stakeholders.
Research & Innovation: Stay updated on AI trends, multi-modal architectures, and best practices; explore new AI methodologies.
Documentation & Reporting: Document AI models, processes, and workflows; prepare project reports and presentations.
