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AI Engineer with less than a year in LLM-powered systems and Generative AI applications
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2026 Computer Science & Engineering graduate student focused on AI automation and Generative AI applications. Experienced in building LLM-powered systems, intelligent workflows, and decision-support tools that convert unstructured data into actionable insights.
Rajiv Gandhi Institute of Petroleum Technology, Bengaluru
B.Tech-CSE · Computer Science & Engineering
August 1, 2022 – June 30, 2026
366Pi Technologies
Engineering Intern
May 1, 2026 – Present
Bengaluru, Karnataka, India
EkamApps
Software Engineer
May 1, 2026 – Present
India
Low-Latency Order Matching Engine (Exchange Core)
June 3, 2026 – Present
Low-latency exchange core in Python replicating order book mechanics using FastAPI, MongoDB, WebSockets. Implemented central limit order book supporting Market, Limit, IOC, FOK orders with price-time (FIFO) matching. Ensured deterministic trade execution using efficient in-memory data structures with strict ordering guarantees. Designed in-memory order book using SortedDict & deque, enabling O(log N) access and reducing latency by 40%. Built asynchronous REST & WebSocket APIs using FastAPI for concurrent order processing and real-time updates. Integrated MongoDB for trade persistence & kept the matching engine fully in-memory for low-latency execution.
Multi-Agent Investment Co-Pilot Microservice
June 3, 2026 – Present
AI microservice routing investor queries through specialist agents to monitor, analyze, and protect portfolios. Built multi-agent FastAPI backend with intent classification, safety guard, and SSE streaming across 6+ intents. Designed <1ms pure-local safety guard (zero LLM cost) with 95% harmful recall across financial crime categories. Implemented portfolio health agent computing concentration risk, benchmark alpha, and streaming LLM summaries. Engineered extensible agent router with 2-file agent addition and graceful LLM fallback; zero request crashes. Achieved <1s first-token latency, $0.002/query cost, and 85% routing accuracy with fully mocked pytest suite.
LLM-powered Candidate Ranking System
June 3, 2026 – Present
AI-powered candidate evaluation system, ranks resumes against job descriptions. Built modular pipeline for resume parsing, job processing, and scoring to convert unstructured data into structured. Implemented LLM-based semantic matching with dynamic ATS parsing/scoring to assess candidate-job alignment. Developed explainable AI layer generating score breakdowns, strengths, gaps, and recommendations. Built Streamlit dashboard for ranking, filtering, and CSV export to support efficient hiring decisions.
View ProjectCultural Fit Analysis
The candidate's project diversity, ranging from AI microservices to financial exchange cores, indicates a broad interest in various technical domains and a willingness to tackle different challenges. The involvement in competitive programming and extracurricular activities suggests a proactive, driven individual who can collaborate and lead. The focus on AI automation and Generative AI aligns well with an 'AI Engineer' role, demonstrating a clear career interest and relevant skill development.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills and an ability to translate complex requirements into functional systems. The project descriptions highlight an aptitude for optimizing performance (e.g., <1ms safety guard, O(log N) access, <1s first-token latency) and ensuring system robustness (e.g., graceful LLM fallback, zero request crashes). The experience in designing comprehensive API architectures and documentation suggests good organizational and communication skills within a technical context. The competitive programming achievements indicate a strong analytical mindset and resilience.