AI Engineer with less than a year in LLM Integration & AI Workflows
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Generative AI Engineer with a strong foundation in building AI-powered applications, LLM integration, and orchestrating AI workflows. Highly proficient in Python and developing scalable RAG pipelines using LangChain, vector databases (FAISS), and Prompt Engineering. Adept at deploying robust machine learning backends via FastAPI and Docker in cloud environments. Proven track record of solving complex analytical problems and collaborating across teams to deliver intelligent, production-ready AI solutions.
Vellore Institute of Technology (VIT)
M. Tech · Artificial Intelligence and Machine Learning
August 1, 2024 – June 1, 2026
Loyola-ICAM College of Engineering & Technology (Anna University)
B.E. · Computer Science and Engineering
August 1, 2020 – May 1, 2024
Intel Technology India Pvt. Ltd.
Graduate Technical Intern — AI Engineering
July 1, 2025 – February 1, 2026
Bengaluru, Karnataka, India
Delonix Designs
Full Stack Development Intern
April 1, 2023 – May 1, 2023
Chennai, Tamil Nadu, India
PoliRAG
June 1, 2026 – Present
• Engineered a high-performance Retrieval-Augmented Generation (RAG) pipeline to build an AI-powered compliance application, integrating vector databases (FAISS) for semantic search. • Developed and refined prompt engineering strategies to improve LLM reasoning, strictly enforcing citation-required generative validation to achieve a hallucination rate of <15%. • Deployed the AI application using FastAPI and Docker, leveraging cloud fundamentals to architect an observable, scalable backend infrastructure with Prometheus monitoring.
View ProjectFinTech Conversational AI Chatbot
June 1, 2026 – Present
• Built and deployed an AI-powered chatbot tailored for FinTech, leveraging Large Language Models (LLMs) to translate complex financial queries into scalable solutions. • Implemented LLM workflows using LangChain to achieve 95%+ intent extraction and semantic accuracy across user interactions. • Integrated real-time multimodal capabilities via Whisper APIs, demonstrating analytical thinking to reduce voice-to-text latency by 40%.
View ProjectMCP Server POC (AI Agents & API Integration)
June 1, 2026 – Present
• Designed and deployed an Agent Tool Gateway using the Model Context Protocol (MCP) to standardize interactions between AI agents and external APIs. • Worked extensively on multi-agent systems, enabling dynamic tool selection with <100ms dispatch latency for LLM-based intelligent agents. • Developed automated evaluation loops to test LLM tool-calling accuracy, applying strong problem-solving skills to mitigate prompt injections and track model reasoning.
View ProjectAWS Solutions Architect
Amazon Web Services
June 1, 2026 – Present
AWS AI Practitioner
Amazon Web Services
June 1, 2026 – Present
Kubernetes
Udemy
June 1, 2026 – Present
Adaptive Multi-Agent Generative AI System (92% F1-score)
Unknown
June 1, 2026 – Present
Hardware-Accelerated SAR Semantic Downlink
Unknown
June 1, 2026 – Present
Zero-Shot Land Cover Classification of SAR-like Imagery using Vision-Language Models (VLMs)
IEEE GeoScience and Remote Sensing Letters
June 1, 2026 – Present
Meta FAISS PR #4792
Meta
June 1, 2026 – Present
Google DeepMind Gemma PR #590
Google DeepMind
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's project diversity, ranging from compliance applications (PoliRAG) to FinTech chatbots and multi-agent systems, indicates a broad interest and adaptability. Their contributions to open-source projects and publications suggest a proactive and learning-oriented mindset, which aligns well with innovative and collaborative cultures. The target role of 'AI Engineer' is well-aligned with their demonstrated skills and project focus. However, the experience level is low, which might impact immediate cultural integration into a senior role without mentorship.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills, analytical thinking, and cross-functional collaboration, as evidenced by project descriptions and experience. Their involvement in Agile environments suggests adaptability and teamwork. The ability to troubleshoot features and refine codebase efficiency indicates a practical and operational fit for development roles.