AI Engineer with less than a year in AI agents, LLMs, and data science workflows.
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AI Engineer with 6 months of hands-on experience in developing and deploying production-grade multi-agent AI systems. Proven ability to design hierarchical intent routing pipelines, build scalable data processing solutions using Polars and DuckDB, and automate end-to-end ML workflows. Proficient in LLMs, RAG, FastAPI, Python, and real-time inference pipelines, with a focus on delivering robust and efficient AI solutions for diverse applications.
Madhav Institute of Technology and Science (MITS DU)
B.Tech · Electronics
August 1, 2023 – June 30, 2027
Autonomous AI Data Science Agent
March 1, 2026 – April 1, 2026
Architected a production-grade multi-agent data science system enabling end-to-end ML workflows (EDA → feature engineering → model training → explainability) through natural language inputs. Designed a hierarchical intent routing pipeline (regex → SBERT → heuristics) within a modular orchestration layer to dynamically trigger tools, manage execution flow, and coordinate agent interactions. Built scalable pipelines handling 70K+ row tabular datasets, with automated model selection (AutoGluon), hyperparameter tuning (Optuna), and integrated SHAP/LIME explainability for interpretable outputs. Optimized data processing using Polars + DuckDB, enabling efficient in-memory transformations and analytical query execution. Automated the complete ML and Data Science lifecycle, eliminating manual intervention across preprocessing, training, and evaluation stages, with real-time feedback and structured outputs. Implemented a CLI-driven execution layer enabling structured data science workflows via terminal commands, with integrated session lifecycle management and streaming-based feedback. Designed an NPM launcher system that provisions isolated Python environments, resolves runtime dependencies dynamically, and orchestrates communication with a FastAPI service for real-time pipeline execution. Validated the system across multiple end-to-end workflows on real-world datasets, ensuring robustness and consistent execution.
View ProjectKreta Bandhu - Real-Time AI Voice Agent for E-Commerce
December 1, 2025 – January 1, 2026
Built a real-time voice AI inference pipeline using Gemini 2.5 Flash with bidirectional streaming, achieving sub-50 ms interrupt latency via AudioWorklet-based processing and efficient model serving. Developed and deployed a scalable e-commerce backend supporting 100+ concurrent sessions, with real-time inventory validation, automated invoicing, and 99.9% system uptime using Node.js and SQLite. Implemented a validation layer for LLM outputs using structured schema enforcement, reducing error rates to <1% in production workflows, while maintaining 60 FPS WebGL frontend performance.
Autonomous AI Ticket Classification & Resolution System
September 1, 2025 – October 1, 2025
Led AI agent development on a 3-member team; built RAG-powered ticket resolution with LangChain that cut response time by ~30% over baseline. Achieved ~92% classification accuracy using fine-tuned BERT + DeBERTa; ReAct reasoning loop autonomously closed resolved tickets, saving ~25% manual effort. Deployed real-time FastAPI backend with WebSocket communication, enabling live ticket updates and a responsive user experience.
Oracle Cloud Infrastructure 2025 Certified Generative AI Professional
Oracle
August 1, 2025 – Present
Machine Learning Specialization
Stanford Online (Coursera)
August 1, 2025 – Present
Cultural Fit Analysis
The candidate's academic projects demonstrate a strong interest and practical application in cutting-edge AI technologies, particularly multi-agent systems and LLMs, which aligns perfectly with an AI Engineer role. The diversity of projects (data science, e-commerce voice agent, ticket resolution) showcases adaptability and a broad skill set. Participation in hackathons and certifications further indicates a proactive learning attitude and commitment to the field.
Soft Skills & Operational Fit
The candidate's project descriptions indicate strong problem-solving abilities, a proactive approach to system design, and an understanding of production-grade requirements. The leadership role in one project suggests teamwork and initiative. The focus on real-time systems and performance optimization aligns well with operational excellence.