Balaji R
AI EngineerAI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI Engineer with less than a year in Generative AI & LLM Applications
AI Engineer specialising in Generative AI application development — RAG pipelines, LLM agents, and multi-agent orchestration using LangChain, LangGraph, and Model Context Protocol (MCP). Delivered four end-to-end production-facing AI systems: a Text-to-SQL analytics agent, an autonomous multi-agent research system, an intelligent document retrieval (RAG) assistant, and a live MCP server integrated with Angel One SmartAPI for real-time trading operations. Experienced with Retrieval-Augmented Generation (RAG), prompt engineering, LLM evaluation, semantic search, and vector databases (ChromaDB, Pinecone). Actively expanding into HuggingFace Transformers and fine-tuning workflows. Available immediately for AI Engineer roles in Bengaluru or remote.
Sri Sairam College of Engineering, VTU
B.E. · Artificial Intelligence & Machine Learning
August 1, 2021 – June 30, 2025
PySpiders
AI & Data Engineering Intern
February 1, 2025 – November 1, 2025
Bengaluru, Karnataka, India
Angel One MCP Server — Live Trading AI Integration
June 24, 2026 – Present
Reduced broker API integration time from days to minutes by architecting a 17-tool FastMCP server that exposed live portfolio, order management, and market data endpoints to Claude via the Model Context Protocol (MCP), enabling full natural-language trading operations. Validated end-to-end LLM-to-API reliability across real-time fund balance retrieval and live order flows, achieving zero data-loss tool calls in Claude Desktop integration testing — establishing a reusable MCP pattern applicable across any brokerage API.
View ProjectFinQuery — AI-Powered Financial Analytics Platform
June 24, 2026 – Present
Enabled non-technical users to query a 20-stock NSE financial database using plain English by building a Text-to-SQL AI agent with LangChain's SQLDatabaseToolkit and a LangGraph create_react_agent loop, achieving validated query accuracy across 3 relational tables (stock_prices, companies, stock_metrics). Reduced end-to-end query-to-visualisation latency to under 3 seconds by engineering a FastAPI backend with Streamlit frontend and Plotly chart rendering, integrating conversational memory for multi-turn financial analysis sessions.
View ProjectDeepResearch Agent — Autonomous Multi-Agent Research System
June 24, 2026 – Present
Automated multi-source research synthesis by designing a 4-agent LangGraph workflow (planner → researcher → extractor → report-writer) that produces citation-backed reports from live web data, cutting manual research time from hours to under 5 minutes per topic. Improved report delivery responsiveness by integrating FastAPI Server-Sent Events (SSE) streaming with a Streamlit frontend, enabling real-time token-level output and reducing perceived wait time to near-zero for end users.
View ProjectDocument Cortex — Intelligent RAG Document Assistant
June 24, 2026 – Present
Built a production-ready Retrieval-Augmented Generation (RAG) pipeline supporting PDF, DOCX, and TXT ingestion, achieving semantically accurate context retrieval from ChromaDB vector store using HuggingFace sentence-transformer embeddings and LangChain orchestration. Improved document QA response quality by implementing LLM evaluation checkpoints that validated retrieval relevance before generation, reducing hallucinated answers and increasing user trust in AI-returned citations.
View ProjectBuilding with Claude
Anthropic
May 1, 2026 – Present
Associate AI Engineer for Developers
DataCamp
November 1, 2025 – Present
Achieved 82% on the Data Scientist — Artificial Intelligence exam, indicating a solid understanding of the subject matter and strong problem-solving capabilities within this domain.
Strengths
Limitations
Cultural Fit Analysis
The candidate's portfolio showcases a strong passion for AI and a proactive approach to learning and applying new technologies, which aligns well with an innovative and fast-paced AI engineering environment. The diversity of personal projects (trading, finance, research, document QA) demonstrates intellectual curiosity and a broad interest in applying AI across different domains. The certifications further indicate a commitment to continuous learning and staying current with industry trends. The candidate's focus on building end-to-end systems suggests a product-oriented mindset.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a strong problem-solving orientation and an ability to translate complex technical challenges into tangible solutions. The focus on reducing integration time, improving query accuracy, and automating research processes suggests a results-driven approach. While direct soft skill assessment is limited, the project descriptions imply good communication of technical outcomes and an understanding of user needs (e.g., non-technical users, real-time output). The internship experience also points to collaboration in a professional setting.