Generative AI Engineer with 1+ years in Prompt Engineering & RAG
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Highly motivated and technical B.Tech Computer Science graduate (2026) seeking the Generative AI Engineer role at Polluxa. Strong foundations in Python and backend API design, with practical experience building conversational chatbots, optimizing structured Prompt Engineering parameters, and deploying production-grade RAG retrieval systems. Proven expertise utilizing LangChain and LangGraph to build autonomous multi-agent pipelines, configuring vector databases like Qdrant, and containerizing APIs using FastAPI and Docker on cloud architectures.
Lakshmi Narain College of Technology
B.Tech · Computer Science Engineering
August 1, 2022 – June 30, 2026
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Multi-Agent GenAI Platform (LangGraph)
January 1, 2024 – Present
Developed an autonomous multi-agent platform using LangGraph featuring 4 specialized agents collaborating on workflow routing. Configured real-time API integrations and session memory management to resolve prompts with an average latency of 2.2 seconds. Containerized all microservices with Docker and set up automated pipelines for deployment on cloud virtual instances.
View ProjectH-002 Customer Support Automation Bot (Hackathon Finalist)
January 1, 2024 – Present
Developed a customer support chatbot using Claude LLM and structured prompt parameters within a strict 48-hour hackathon limit. Awarded Top 5 Finalist out of 100+ teams at the GroundTruth AI Hackathon for innovative design and robust workflow execution.
View ProjectMediVision Conversational AI Assistant (Healthcare Chatbot)
January 1, 2024 – Present
Built an interactive chatbot using Gemini LLM and LangChain to extract medical insights from text documents. Designed a vector-based RAG pipeline utilizing semantic similarity search, achieving a retrieval precision score of 92%. Excluded sensitive PHI/PII details, deploying the backend as a secure FastAPI microservice containerized with Docker.
View ProjectCultural Fit Analysis
The candidate's diverse project portfolio, including a hackathon finalist project and a healthcare chatbot, indicates a proactive and innovative approach. Their experience with various AI frameworks and deployment tools aligns well with a dynamic Generative AI environment. The focus on practical, real-world applications and performance optimization suggests a good fit for a results-driven culture. The candidate is still pursuing their B.Tech, indicating a strong learning curve and potential for growth.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through project work and hackathon participation. Their experience in automating data pipelines and optimizing workflows suggests an operational mindset. The ability to work on diverse projects (healthcare, customer support) indicates adaptability. However, without direct assessment data on communication or teamwork, these aspects are inferred from project descriptions.