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AI Engineer with 1+ years in agentic AI solutions, MLOps, and LLM fine-tuning
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Assessing your cultural and operational fit
AI/ML Engineer with 1 year of hands-on experience building and deploying production-ready agentic AI solutions, MLOps pipelines, LLM fine-tuning systems, and RAG evaluation frameworks. National-level hackathon winner at Graviton for an AI-driven phishing URL detection system and Best Paper Award winner at IEEE ICOMMS 2025 as a co-author of the paper "Reliable V2V Computation Offloading Using Deep Reinforcement Learning." Proven ability to improve model accuracy (62% → 85% on NL2SQL), reduce extraction latency from 5 minutes to 90 seconds, and deliver end-to-end AI automation across enterprise environments using LangGraph, MCP tool integration, intelligent document processing, and ML monitoring/retraining.
PES University, Bengaluru
B.Tech · Electronics and Communication Engineering
N/A – June 30, 2025
Oracle
Associate Consultant
July 1, 2025 – Present
Bengaluru, Karnataka, India
4 Good AI
Full Stack Developer Intern
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Bengaluru, Karnataka, India
Simtech IT Solutions
Machine Learning Intern
June 1, 2024 – August 1, 2024
Bengaluru, Karnataka, India
State Level Hackathon Winner
Graviton
June 1, 2026 – Present
Research Publication
IEEE ICOMMS 2025
January 1, 2025 – Present
Cultural Fit Analysis
The candidate's project diversity, ranging from expense automation and document processing to NL2SQL fine-tuning and pothole detection, indicates a broad interest and adaptability to different problem domains. Their involvement in a hackathon and research publication suggests a proactive and innovative mindset. The experience with various tools and platforms (OCI, LangGraph, MLflow, Jenkins, React, Node.js) shows a willingness to learn and integrate new technologies, aligning well with a dynamic AI engineering environment. The focus on practical, production-ready solutions demonstrates a results-oriented approach.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through their project descriptions, tackling complex real-world problems like manual reimbursement workflows and document review processes with AI solutions. Their experience with MLOps, observability, and distributed systems indicates an understanding of operational best practices and system reliability. The collaborative nature of their projects (e.g., multi-agent pipelines, multi-worker systems) suggests good team collaboration potential.