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AI Engineer with less than a year in AI Automation & LLM Agents
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Assessing your cultural and operational fit
Final-year Data Science student with expertise in Python, Machine Learning, and Generative AI. Actively engaged in hands-on projects, including the development of a Corrective RAG platform that enhances AI application reliability. Demonstrated ability to implement advanced retrieval techniques, showcasing a commitment to innovative AI Automation and workflow integration.
PUCIT
BSc Computer Science · Data Science
August 1, 2023 – June 30, 2027
PUCIT
Teaching Assistant | Trainee
January 1, 2026 – June 1, 2026
Lahore, Punjab, Pakistan
AI-Powered GitHub Code Review Agent
June 1, 2026 – Present
• Engineered an async processing layer with Celery and Redis for non-blocking webhook handling under concurrent load. • Integrated a SQLAlchemy/Alembic data layer across PostgreSQL and SQLite for dev/prod parity; in final testing for latency benchmarking.
View ProjectWattSun - AI-Powered Smart Energy Monitoring & Advisory System
June 1, 2026 – Present
• Designing a full-stack agentic energy-advisory system, from an ESP32 + PZEM-004T sensing layer to a LangGraph-orchestrated advisory agent. • Architecting bilingual (Urdu/English) AI chat against real Pakistani electricity tariffs (LESCO, IESCO, MEPCO, FESCO), with PaddleOCR bill parsing. • In active development as a four-member team under faculty supervision (Dr. Muhammad Arif Butt); architecture and hardware integration complete.
View ProjectResearch & Blog Crew
April 1, 2026 – Present
• Architected a 7-agent CrewAI pipeline (Planner, Researcher, Fact-Checker, Writer, Editor, SEO Specialist) for automated content generation. • Produces fact-checked, SEO-optimized articles from a single topic with zero manual intervention. • Deployed on Render with Groq-hosted Llama 3.3-70B for fast, cost-efficient inference at scale.
View ProjectCorrective RAG (CRAG) Platform
January 1, 2026 – January 1, 2026
• Built a production implementation of the Corrective RAG paper (Yan et al., 2024) with a FAISS retrieval step graded by cosine-similarity confidence. • Routes low-confidence results through query rewriting and live DuckDuckGo web-search fallback before generation, eliminating hallucination on out-of-knowledge-base queries. • Shipped multi-tenant document isolation with JWT/bcrypt auth and a real-time pipeline-log UI exposing each CRAG decision step. • Containerized with Docker and deployed as a split Vercel (React) + Railway (FastAPI) production stack.
View ProjectDentalScribeAI
January 1, 2026 – January 1, 2026
• Built a real-time clinical scribe transcribing dentist-patient conversations via Groq Whisper with word-level timestamps. • Used dental-specific prompting to extract structured chart findings with verbatim evidence citations. • Implemented vectorless RAG over a hierarchical dental knowledge tree (PageIndex SDK) paired with a Next.js citation UI. • Shipped a 5-endpoint FastAPI backend as part of a 3-person team, owning the RAG and backend layers.
TubeChat AI
January 1, 2026 – February 1, 2026
• Built and deployed a YouTube RAG chatbot that chunks and embeds any video transcript (HuggingFace sentence-transformers) and answers questions via a LangChain + FAISS retrieval pipeline. • Integrated 4 LLMs via the Groq API with multilingual prompt engineering, live on Streamlit Cloud with persistent chat history.
View ProjectCultural Fit Analysis
The candidate's diverse personal and academic projects, particularly those addressing real-world problems (e.g., WattSun, DentalScribeAI), indicate a strong drive for innovation and practical application of AI. The involvement in open-source tools and modern AI paradigms (agentic AI, CRAG) suggests an alignment with a forward-thinking, collaborative, and technically curious culture. The teaching assistant role also points to a willingness to share knowledge and contribute to a learning environment.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a proactive and hands-on approach to learning and problem-solving. The academic project 'WattSun' shows experience in team collaboration under faculty supervision. The teaching assistant role suggests communication and mentorship skills. However, the overall experience level is low, which might impact operational fit in a senior role requiring extensive independent decision-making and leadership.