AI Research Engineer with less than a year in LLM, RAG & FastAPI
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Final-year AIML engineer with experience at Samsung R&D building LLM evaluation pipelines, RAG systems, and FastAPI-based AI applications. Skilled in LangChain, LangGraph, prompt engineering, and MLOps, with a strong interest in agentic AI, scalable deployment, and reliable AI systems.
Cambridge Institute Of Technology
Bachelor of Engineering · AIML
N/A – June 30, 2026
Samsung Research and Development
AI Intern
September 1, 2025 – May 1, 2026
Bengaluru, Karnataka, India
Ekshauk
Data Analyst Intern
January 1, 2024 – April 1, 2024
India
SHL Assessment Recommendation Agent
June 1, 2026 – Present
Built and deployed a conversational agentic AI system using RAG (LangChain + ChromaDB) and Groq LLM, enabling semantic retrieval, multi-turn clarification workflows, and personalized SHL assessment recommendations. Designed agent decision logic for catalog retrieval, recommendation ranking, and refinement flows; exposed REST APIs (/chat, /health) via FastAPI and deployed on Hugging Face Spaces
View ProjectMulti-Agent Research Assistant
June 1, 2026 – Present
Designed and built a 4-agent autonomous research pipeline using CrewAI — comprising a Research Agent, Analysis Agent, Summary Agent, and Report Generation Agent. Integrated RAG (LangChain + ChromaDB) with Groq LLM for intelligent information retrieval, deduplication, and structured report generation.
View ProjectHackathon Winner (Data Insights)
Unknown
June 1, 2026 – Present
The candidate achieved a perfect score (100%) on the 'Data Scientist — Artificial Intelligence' exam, indicating comprehensive knowledge and proficiency in the evaluated skills.
Strengths
Cultural Fit Analysis
The candidate's projects demonstrate a strong interest in cutting-edge AI technologies like agentic AI, RAG, and LLMs, which aligns well with an AI Research Engineer role. The diversity of projects (recommendation agents, research assistants) and internship experiences (LLM evaluation, data analysis) shows adaptability and a broad skill set. The focus on practical deployment and optimization suggests a candidate who can contribute effectively to a research and development environment.
Soft Skills & Operational Fit
The candidate's project descriptions and internship experiences suggest a proactive and results-oriented individual. The ability to design and implement multi-agent systems and optimize workflows indicates strong problem-solving and analytical skills. The psychometric test score (353/500) suggests a moderate fit in areas like logical reasoning and work attitude, which could be further explored during interviews. The English test score (81/100) indicates good communication clarity.
Limitations