Andrew H.

Looking to supercharge your LLM/ChatGPT app or kick off a brand-new project with a bang? You've come to the right place! I'm all about creating next-level AI applications centered around Large Language Models (think GPT, Anthropic, Llama, and more). I've packed my toolkit with some of the coolest, most advanced features in AI, Retrieval-Augmented Generation (RAG), and Natural Language Processing (NLP) to tackle specific domains and complex challenges head-on. Let's take your AI game to the next level! My process to future-proof your business involves, 1. Setting up your data in the best possible format. 2. Optimizing your AI infrastructure for a more deterministic (yet creative) flow in the probabilistic domain of AI. 3. Developing a scalable architecture that anticipates growth. 4. Delivering cost-effective solutions and long-term support. 5. Prioritizing Data Security and Compliance at each increment. Currently, I along with some individuals am in the process of launching a PaaS-based startup revolving around building, evaluating, deploying, and managing LLM/ML-based applications for production environments. As a contributor to Langchain, LlamaIndex, and other supporting frameworks, I've significantly engaged with the open-source LLM/RAG library space and continually participate in the LLM/AI community. My Expertise lies in custom AI Assistants, Chatbots, and Workflow Automations to make your business/product 10x more efficient. Advanced proficiency in the following domains, ✦ Model Deployment ✦ Model Training ✦ Model Fine-tuning ✦ Knowledge Extraction ✦ Ad Hoc Structured Data Query ✦ RAG Enhancement ✦ Prompt Engineering ✦ Performance Analysis ✦ LLM Evaluation Core Competencies ● AWSxAzure Administrator & Solutions Architect ● ChatGPT/OpenAI-Based RAG Applications ● LLMOps (Architecture/Development): - Langchain, LlamaIndex, Ragas, Haystack, HuggingFace Sentence Transformer, Ollama - Any vector database, (Pinecone, ChromaDB, Milvus, Qdrant, Weaviate, Azure Search) - AWS SageMaker, Amazon Bedrock, Amazon Q, Azure OpenAI & AI Studio, - Training & Fine-tuning: DPO, RLHF, PPO, PEFT (LoRA, QLoRA) ● Python Backend (Flask, Django, FastAPI, Streamlit, Dash, PyQT & Tkinter for GUI) ● Datastores: MongoDB, PostgreSQL, Redis, DynamoDB, Elasticsearch, Azure CosmosDB ● Linux Server Administration (Amazon-Linux, Debian-based, RHEL), ● Web Servers: Apache2, Nginx ● Containerization and Orchestration: Docker, Docker Swarm, ECS, Kubernetes (Across major Cloud Vendors) ● AWS Serverless: API Gateway, Lambda, SNS, CloudWatch, CloudFront, S3, DynamoDB ● IaC: Cloudformation, Terraform, Ansible ● CI/CD: Jenkins, GitHub Actions, AWS CodeDeploy/CodeBuild, GitLab. ● Real-Time Data and API Integration If you are trying to build an MVP for an AI Project my guide to you would be, "The best way to start is just by building on top of . . . whatever the best model is . . .. Don’t worry about [cost or latency] at first. You’re really just trying