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AI Engineer with less than a year in RAG & Multi-Agent Systems
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Final-year Software Engineering student specializing in backend AI engineering, Retrieval-Augmented Generation (RAG), and multi-agent systems. Experienced building end-to-end AI applications using FastAPI, LangChain, LlamaIndex, FAISS, Docker, Redis, and multiple LLM providers (OpenAI, Gemini, Groq, LLaMA, Mistral). Passionate about designing scalable AI infrastructure, agent orchestration, and production-ready backend services.
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TruthLens: RAG-Based Fake News Detection
June 28, 2026 – Present
Built an end-to-end RAG pipeline using FAISS vector search and Sentence Transformers (all-MiniLM-L6-v2) for evidence retrieval prior to LLM inference, improving retrieval quality and response grounding. Evaluated embedding models, retrieval strategies, and prompt variations to improve semantic retrieval quality and factual grounding; indexed documents using FAISS with chunk-size experimentation. Integrated DistilBERT and Mistral-7B-Instruct for claim classification and explainable natural language output generation grounded in retrieved context.
Integrow: Multi-Agent SDLC Automation System
June 28, 2026 – Present
Built a multi-agent AI system orchestrating SDLC tasks through specialized agents with rule-based LLM routing across OpenAI, Gemini, and Groq based on task requirements and latency considerations. Designed persistent conversation memory for maintaining context across multi-step agent interactions; implemented structured JSON outputs and prompt templates for consistent tool usage across LLM providers. Developed FastAPI inference endpoints for AI agents with structured JSON responses, retry handling, and robust error management; integrated Redis for caching and workflow state management. Containerized FastAPI AI services using Docker and exposed modular REST endpoints for agent interaction; configured GitHub Actions to automate testing and deployment workflows.
View ProjectAspect-Based Sentiment Analysis
June 28, 2026 – Present
Fine-tuned RoBERTa transformer for multi-aspect sentiment classification; optimized with AdamW on GPU-accelerated PyTorch across positive, negative, and neutral classes. Built full ML pipeline covering data preprocessing, tokenization, training, evaluation, and iterative prompt-level optimization.
View ProjectE-Commerce Web Application
June 28, 2026 – Present
Built full-stack application with REST API backend, role-based access control, JWT authentication, and dynamic data handling; designed and tested APIs using Postman.
View ProjectFinalist, UI/UX Design Competition
FAST University
January 1, 2024 – Present
Participant, Multiple Speed Programming Competitions
Unknown
January 1, 2022 – Present
Cultural Fit Analysis
The candidate's diverse personal projects (fake news detection, SDLC automation, sentiment analysis, e-commerce) demonstrate a strong initiative and passion for applying AI and software engineering across different domains. The target role of 'AI Engineer' aligns well with the candidate's project focus and stated specialization. The breadth of technologies used (various LLMs, backend frameworks, DevOps tools) suggests adaptability and a continuous learning mindset, which are positive indicators for cultural fit in a dynamic AI environment. However, the limited professional experience (internship and short-term freelance roles) means less exposure to large-scale team dynamics and corporate culture.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to work on complex, multi-faceted problems (e.g., multi-agent systems, RAG pipelines). Collaboration with clients and backend teams suggests good interpersonal skills. The focus on performance, maintainability, and robust error management points to a detail-oriented and quality-conscious approach. Participation in speed programming competitions indicates problem-solving aptitude under pressure.