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AI Engineer with 2+ years in LLM Systems & RAG
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Software Engineer with 2+ years of production experience, transitioning into GenAI engineering through hands-on project work in LLM-powered systems, RAG pipelines, and agentic AI. Built end-to-end GenAI projects using LangChain, LangGraph, Qdrant, Sentence Transformers, and Groq-hosted Llama models — covering document ingestion, vector retrieval, multi-agent orchestration, and streaming interfaces. Strong software foundation across FastAPI, Vue.js, Docker, Azure, and CI/CD, with a 9.38 GPA in AI & Data Science and a published IEEE deep learning paper.
Seshadri Rao Gudlavalleru Engineering College
B.Tech · Artificial Intelligence & Data Science
August 1, 2020 – April 1, 2024
Finmkt
Software Engineer
January 1, 2024 – Present
Hyderābād, Telangana, India
NexusAI — Enterprise Multi-Agent AI & Conversational Platform
June 24, 2026 – Present
Built an enterprise-style conversational AI platform using FastAPI, Vue.js, LangGraph, Qdrant, and Groq-hosted Llama 3.3 for document-grounded question answering with real-time streaming responses. Developed a complete RAG pipeline including PDF ingestion, recursive text chunking, embedding generation with Sentence Transformers, and vector retrieval using Qdrant for accurate, grounded answers. Implemented multi-agent workflows using LangGraph with router, retrieval, summarisation, and critic nodes to improve answer grounding and response quality across diverse query types. Designed role-based access control (RBAC) retrieval by attaching document metadata and applying Qdrant filters to enforce department-level access restrictions — preventing cross-role data leakage. Developed a streaming chat interface with SSE-based real-time responses, source citations, agent execution trace visualisation, and session history for improved transparency and user experience.
View ProjectReddit Freelance Job AI Bot — Agentic Automation Pipeline
June 24, 2026 – Present
Built a fully automated agentic pipeline — Reddit scraper → LangChain RAG (FAISS vector store) relevance filter → Ollama/Mistral LLM → n8n workflow automation → Telegram delivery — generating personalised, human-like proposal drafts with zero cloud API cost. Reduced proposal drafting time from ~30 minutes to under 2 minutes per job via automated context retrieval and structured few-shot/zero-shot prompt engineering — validated on 50+ live Reddit job posts. Designed modular prompt engineering framework separating system context, retrieval context, and user instruction layers — improving response consistency across diverse job categories. Dockerised the complete pipeline as a single-command local service, integrating Ollama, n8n workflow engine, and Telegram Bot API with no external dependencies.
View ProjectCultural Fit Analysis
The candidate's project diversity, ranging from an enterprise conversational AI platform to a personal automation bot, indicates a strong passion for AI and a proactive learning attitude. The blend of professional software engineering experience with dedicated AI project work shows a clear career trajectory towards AI engineering. The academic background in AI & Data Science further reinforces alignment with an AI-focused role. The candidate appears to be a self-starter and capable of independent project execution.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through complex project implementations (e.g., RBAC retrieval, multi-agent workflows). The ability to work with diverse technologies and integrate them into functional systems suggests adaptability and a proactive approach. Experience with CI/CD and Docker indicates an understanding of operational best practices. The detailed project descriptions imply good communication of technical concepts.