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
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 ProjectLow-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.
Cultural 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.