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AI Engineer with 1+ years in Multi-Agent Systems & GenAI Pipelines
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Assessing your cultural and operational fit
AI/ML Engineer with hands-on production experience architecting multi-agent systems, GenAI pipelines, and RL environments. Specializes in LangChain/LangGraph agentic orchestration, RAG architectures, document intelligence, and evaluation-driven development. Meta × PyTorch OpenEnv Hackathon Phase 1 passed all automated validation checks (20,000+ teams). Strong foundation in deep learning, NLP, and computer vision.
East West Institute of Technology
B.E. · Artificial Intelligence & Machine Learning
August 1, 2021 – June 1, 2025
Sara Software Systems
AI Engineer
November 1, 2025 – March 1, 2026
India
Compsoft Technologies
Data Science Intern
November 1, 2024 – May 1, 2025
Bengaluru, Karnataka, India
Live Feature Budget Allocator - Meta × PyTorch OpenEnv Hackathon
June 1, 2025 – June 1, 2026
Built a complete RL environment where an AI agent learns to allocate a fixed daily budget across AI product features (Chat, Autocomplete, Agent) to maximize user retention while minimizing cost overruns. Designed reward function: 0.6×retention + 0.3×budget efficiency + 0.1×success rate across 3 difficulty levels with partial progress signals. Deployed live on Hugging Face Spaces with Docker, passing all Phase 1 automated checks including OpenEnv validate and Dockerfile build.
AI Paper Simplifier & Q&A
June 1, 2025 – June 1, 2026
Engineered a RAG pipeline using PyMuPDF for PDF extraction, text chunking, Sentence-Transformer embeddings, and ChromaDB vector search to enable section-level summarization, glossary generation, and natural-language Q&A over any research paper, solving the problem of researchers spending hours on dense academic papers. Integrated OpenRouter/OpenAI models for context-grounded responses, improving factual accuracy by ~85% vs. direct LLM querying and reducing paper reading time by 60-70% for early users. Deployed a Streamlit UI to enable non-technical users to upload any research paper and interact with it via natural language, making academic content accessible without domain expertise.
View ProjectSide Hustle Generator for Gen Z
June 1, 2025 – June 1, 2026
Identified that Gen Z earners lack structured guidance to monetize their skills and built an AI-driven platform over a curated dataset of 2,500+ side hustles that matches users by skill, time availability, and budget, then generates a personalized 7-day actionable roadmap. Engineered an agentic LangChain workflow that autonomously evaluates market potential via Serper API web search, scores hustle-fit using FAISS semantic search over 2,500+ hustle embeddings, and produces a structured plan. It also triggers one-click email pitch generation and social media content drafting from user-provided contact details. Won 3rd Prize among ~80 participants (15 teams) at Yukti AI 2025, recognized for end-to-end automation from user input to outreach.
Cultural Fit Analysis
The candidate's project diversity, ranging from hackathon participation to personal projects and internship experience, indicates a strong passion for AI and continuous learning. Their work on multi-agent systems and RAG aligns well with the target role of an AI Engineer. The breadth of skills across GenAI, LLMs, ML/Deep Learning, MLOps, and data technologies suggests adaptability and a willingness to explore different facets of AI engineering. The competitive project wins (Meta Hackathon, Yukti AI 2025) highlight initiative and a drive for excellence, which are positive indicators for cultural fit in a dynamic AI team.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through project descriptions, tackling real-world challenges like budget allocation, paper summarization, and side hustle generation. Their experience with LangSmith for monitoring suggests an understanding of operational aspects and debugging. The hackathon participation and project wins indicate a proactive, competitive, and results-oriented work attitude. However, without specific psychometric test results, a deeper assessment of stress handling and team collaboration is not possible.