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AI Engineer with less than a year in LLMs & RAG Systems
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Assessing your cultural and operational fit
AI/ML Engineer with 4 months of combined experience in developing RAG chatbots, ingestion pipelines, and retrieval evaluation frameworks. Proficient in Python, FastAPI, LangChain, and various LLMs (Qwen, Kimi, Phi-3). Strong background in NLP, deep learning, and cloud deployments (AWS). Successfully built and deployed an AI contract analysis platform, demonstrating expertise in practical AI/ML applications and scalable backend development.
National University of Computer and Emerging Sciences (FAST-NUCES)
Bachelor of Science · Computer Science
August 1, 2022 – June 30, 2026
EvantageSoft Pvt Ltd
AI Engineer Intern
April 1, 2026 – Present
India
Nexium
AI Intern
June 1, 2025 – August 1, 2025
India
DocuShield
June 1, 2026 – Present
Built a legal contract analyzer supporting multiple document formats with automated risk classification using NLP techniques. Engineered a hybrid RAG system combining Groq-hosted Llama-3-70B with FAISS vector search for sub-2-second contract analysis. Implemented OCR pipeline using AWS Textract for document preprocessing and structured data extraction. Deployed containerized FastAPI backend on AWS (S3, Lambda, EC2) with Docker for scalable document processing.
View ProjectMachine Learning in Production
Coursera
June 1, 2025 – Present
Understanding Machine Learning
DataCamp
February 1, 2025 – Present
Supervised Learning with Scikit-Learn
DataCamp
February 1, 2025 – Present
Cultural Fit Analysis
The candidate's projects and internships are highly aligned with an AI Engineer role, focusing on RAG systems, NLP, and LLMs. The diversity of projects (legal contract analysis, document-based chatbots) and the use of various tools (LangChain, FAISS, Groq, AWS) demonstrate adaptability and a broad interest within the AI/ML domain. The current internship and ongoing education suggest a commitment to continuous learning and growth.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a proactive approach to problem-solving and a focus on practical implementation. The experience in evaluating LLMs and tuning retrieval systems suggests an analytical mindset and attention to detail. The use of FastAPI and Docker points to an understanding of operationalizing AI solutions.