Amandeep K.

I build AI agents, LLM applications, and RAG systems that connect to real data, APIs, and business tools and actually perform useful work. I use Python, OpenAI, Claude, FastAPI, RAG, APIs, databases, and agent architectures to turn AI ideas into reliable products, from internal AI assistants and document systems to customer-facing applications and workflow-driven agents. The focus is on building AI that can understand information, use tools, retrieve data, follow business rules, take actions, and return reliable results, rather than simply generating chat responses. What I Build • AI agents and multi-step agent workflows • LLM-powered applications and AI assistants • RAG systems and company knowledge assistants • Document processing, extraction and analysis • OpenAI & Claude integrations with custom business logic • AI backends using Python & FastAPI • AI systems connected to APIs, CRMs and databases • AI evaluation, reliability and production optimization AI Engineering Experience I work across the complete AI application stack from model and prompt integration to RAG, tool calling, structured outputs, backend APIs, data pipelines, and production deployment. I can build AI systems that work with your documents, databases, internal knowledge, APIs, CRM, or existing application, while keeping accuracy, latency, cost, security, and maintainability in mind. A typical system might look like: User Request → AI Agent → Retrieve Data → Use Tools/APIs → Apply Business Rules → Take Action → Return Result This is where my backend engineering experience becomes important. I can build the supporting APIs, database logic, authentication, background processing, and integrations required to make an AI system work reliably in a real environment. Relevant AI Work Policy Assistant AI POC Built an AI-powered assistant using LangChain, OpenAI, Chroma, FastAPI, and Streamlit, allowing users to work with policy information through an intelligent knowledge interface. Discord RAG Bot Built a RAG-based AI assistant for course support, allowing users to ask questions against relevant course knowledge rather than relying only on general LLM responses. AI Voice Agent - Banking Use Case Worked on an AI voice system using Python, OpenAI APIs, VAPI, and ElevenLabs, connecting conversational AI with voice interactions and backend workflows. These projects demonstrate practical experience across RAG, LLM applications, AI assistants, voice AI, APIs, Python backends, and real-world AI product development. Why Clients Work With Me ✓ Strong AI + Python engineering background ✓ Experience building AI agents beyond basic chatbot implementations ✓ Strong hands-on experience with OpenAI, Claude, RAG and LLM applications ✓ Can connect AI to APIs, databases, CRMs and existing applications ✓ Comfortable building both the AI layer and backend infrastructure ✓ Focus on reliability, maintainability, performance and production readiness ✓ Able to work from an AI concept, prototype, Figma, technical specification, or existing application Technology Stack AI & LLM: OpenAI | Claude | Gemini | AI Agents | RAG | LLM Applications | Structured Outputs Frameworks: LangChain | LlamaIndex | CrewAI | AutoGen Backend: Python | FastAPI | Django | Flask | REST APIs Data: PostgreSQL | MySQL | Redis | Vector Databases Infrastructure: AWS | GCP | Docker | RunPod | vLLM | TGI Integrations: APIs | Webhooks | CRM | SaaS Platforms | Third-Party Services Best Fit I work particularly well with startups building AI products, SaaS companies adding AI to an existing application, businesses that need internal AI assistants, and teams that want to move an AI prototype toward a production-ready system. Typical projects include: AI Agents | RAG Applications | AI Assistants | LLM Applications | Document AI | AI APIs | Voice AI | AI SaaS | AI Backend Development | Existing AI Product Enhancement Whether you need to build an AI agent, create a RAG application, integrate OpenAI or Claude into your product, build an AI assistant, or turn an existing prototype into a production system, I can help take it from architecture through implementation. Send me your AI use case, existing application, prototype, or technical requirements, and I’ll help turn it into a practical production-ready system. Thank you