AI Engineer with less than a year in LLM Agents & RAG Systems
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/ML Engineer with 6 months of production experience building LLM agents, RAG systems, and automation workflows. Developed autonomous calling agents that reduced manual task handling by 60% and RAG pipelines improving answer accuracy by 35%. Proficient in LangChain, LangGraph, Semantic Kernel, CrewAI, AutoGen, FastAPI, and C#/.NET. Seeking AI Engineer roles to build scalable, production-ready intelligent systems.
ITM (SLS) Baroda University
B.Tech · Computer Science
August 1, 2022 – June 30, 2026
McKH Technologies
AI/ML Intern
December 1, 2025 – Present
Vadodara, Gujarat, India
CODECRAFT INFOTECH
Machine Learning Intern
February 1, 2025 – March 1, 2025
India
CodeSoft
Machine Learning Intern
January 1, 2025 – February 1, 2025
India
Apex-Agent (Autonomous Agent | Agentic Assistant)
June 24, 2026 – Present
Architected a Multi-Tiered Sub-Agent (MTSA) framework using LangGraph and FastAPI to orchestrate collaborative AI agents for complex, autonomous problem-solving. Integrated the Model Context Protocol (MCP) to securely connect language models with external data sources, file systems, and specialized local tools. Developed a highly responsive React.js frontend featuring an Interactive Agent Loop (IAL) for real-time task state visualization and dynamic chat interactions.
View ProjectStudy-Buddy – Multi-Agent AI Learning System
June 24, 2026 – Present
Designed a multi-agent AI system using LangGraph, MCP, and A2A for curriculum planning, tutoring, quiz generation, and adaptive learning. Implemented RAG-based knowledge retrieval with local LLMs (Ollama) and persistent session management using SQLite checkpointing. Built and deployed a FastAPI + Next.js application with agent observability (Langfuse) and CrewAI interoperability.
View ProjectHealthcare AI Portal
June 24, 2026 – Present
Built multi-disease prediction models (heart disease, diabetes, lung cancer) using fine-tuned ML classifiers, reaching 87–92% accuracy across all three domains. Developed an interactive medical chatbot (LangChain + RAG) for personalised risk assessment, reducing time-to-guidance compared to static decision trees.
View ProjectAI-Powered Healthcare Voice Agent & CRM
June 24, 2026 – Present
Automated Voice Scheduling: Seamlessly handles appointment bookings and cancellations using a low-latency VAPI + FastAPI voice engine with natural language understanding. Predictive No-Show Analysis: Features a custom risk-scoring algorithm that identifies "High Risk" appointments based on historical lead times and cancellation trends. AI-Driven Admin Intelligence: Provides a Next.js 14 dashboard with real-time analytics, peak-hour visualizations, and automated insights for hospital resource optimization.
View ProjectN8N Automation
June 24, 2026 – Present
Built an autonomous lead qualification calling agent using n8n + Vapi, capable of initiating calls, interacting with users, and updating CRM(sheet) workflows without human intervention. Designed end-to-end agentic workflow automation, including lead filtering, conversation handling, and follow-up scheduling, improving response efficiency. Developed a domain-specific AI relationship assistant leveraging structured workflows and LLM orchestration to deliver contextual, human-like interactions. Engineered RAG-based and RAG 2.0 systems integrating vector databases and LLMs to deliver accurate, context-aware responses from custom knowledge bases.
AI
Anthropic
June 1, 2026 – Present
Gen-AI
IBM
June 1, 2026 – Present
Python
HackerRank
June 1, 2026 – Present
Data Analysis & Visualisation
IBM / Google / Microsoft
June 1, 2026 – Present
AI Engineer
OneRoadmap
June 1, 2026 – Present
LangChain for LLM Application Development
CodeSignal
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's strong focus on personal projects, particularly in emerging AI fields like autonomous agents and multi-agent systems, indicates a high level of self-motivation and a passion for AI. The breadth of technologies and frameworks explored (LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel) suggests a continuous learning mindset. The projects in healthcare and education also show an interest in applying AI for social good, which can align well with mission-driven organizations. However, the lack of team-based project descriptions makes it difficult to assess direct collaboration experience.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a proactive and problem-solving approach, with a focus on building practical, impactful AI solutions. The experience in defining agent evaluation metrics and iteratively tuning prompts suggests an understanding of iterative development and quality assurance in AI. The diverse project portfolio also implies adaptability and a willingness to explore different problem spaces.