Mowlick Armstrong

Computer Science undergraduate (B. Tech, AI & ML) with hands-on Python experience building LLM-powered applications: prompt-engineered generative tools, Retrieval-Augmented Generation (RAG) pipelines, and multi-step conversational workflows using the Gemini API. Comfortable integrating LLMs via REST APIs and reasoning through multi-turn logic; eager to apply this foundation to agent frameworks such as LangChain, CrewAI, and AutoGen.

AI & Prompt Engineering Intern at Vault of Codes (2025-06 – 2025-07)

• Built a Python/Streamlit application powered by the Gemini API, writing prompt logic that branches into different generated outputs based on user-selected choices — a multi-path workflow similar to simple agent decision logic.
• Integrated LLM outputs with Java and C++ backend services via API calls, gaining hands-on experience connecting LLMs into larger applications.

AI Intern — Deep Learning & NLP (Infosys Springboard, Internship 6.0) at Infosys (2025-10 – 2025-12)

• Designed a rule-based conversational workflow in Python that reasons over user input across multiple turns and routes to the correct response path, as part of a team project (AgroBot).
• Connected the conversational logic to a custom-trained CNN model, integrating model inference output into the decision workflow — the same input-to-tool-to-output pattern used in agent pipelines.

B. Tech in Computer Science & Engineering (AI & ML) – Sharda University, Greater Noida (2023-27)