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AI Engineer with less than a year in LLM pipelines, agentic systems, and RAG architectures
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Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
GenAI Engineer with hands-on experience building production-grade LLM pipelines, agentic systems, and RAG architectures. Proficient in LangGraph and LangChain for designing modular, graph-based AI workflows with structured state management. Skilled in integrating generative AI APIs (OpenAI, Gemini, Groq) for high-throughput, contextual inference. Passionate about translating complex AI research into scalable, maintainable backend systems.
J.S.S Academy of Technical Education, Noida
B.Tech · Computer Science
August 1, 2023 – Present
Royal Mount Academy, Lucknow
Intermediate · Science
June 1, 2021 – May 31, 2023
ExamDuo.ai
GenAI Intern
November 1, 2025 – April 1, 2026
India
AI Academic Assistant
April 1, 2026 – Present
Designed an AI assistant that retrieves and synthesises learning resources, understands nuanced user questions, and generates accurate, source-grounded answers tailored to the learner's context. Built end-to-end document processing pipelines covering ingestion, multilingual translation, semantic chunking, and embedding generation – supporting a wide range of educational content formats. Constructed modular AI workflows using LangGraph with explicit state management, enabling reliable and deterministic multi-step reasoning across complex, multi-turn academic queries. Enhanced answer relevance through dense vector search, semantic retrieval strategies, and contextual re-ranking of retrieved documents to surface the most accurate results.
Agentic Chatbot
September 1, 2025 – Present
Designed an autonomous AI chatbot capable of planning, reasoning, and executing multi-step tasks using tool-calling agents with persistent contextual memory across conversation turns. Implemented dynamic LangGraph workflows that adapt execution paths in real time based on intermediate reasoning results, tool outputs, and evolving user intent. Integrated Groq LLM inference for high-speed response generation, enabling smooth real-time conversational performance even during complex, multi-tool reasoning chains. Built an interactive Streamlit interface that visualises the agent's live reasoning process, giving users full transparency into how the AI arrives at its decisions.
View ProjectBlog Generation Bot
September 1, 2025 – Present
Engineered an automated blog generation system using graph-based AI workflows with dedicated nodes for title generation, article writing, and multilingual translation into target languages. Developed a FastAPI backend exposing clean RESTful endpoints for triggering and managing the full content generation pipeline, enabling seamless integration with external clients. Implemented schema-driven structured outputs using Pydantic across all pipeline nodes, ensuring every generated article is consistently formatted and free of malformed content. Designed conditional graph routing to dynamically skip or include pipeline stages based on input parameters, improving overall pipeline efficiency and reducing unnecessary compute.
Cultural Fit Analysis
The candidate's projects demonstrate a strong interest and hands-on experience in cutting-edge AI technologies, particularly in agentic systems and RAG, which aligns well with an AI Engineer role. The diversity of projects (academic assistant, chatbot, blog generation) shows adaptability and a broad application of AI principles. The use of various LLM APIs (OpenAI, Gemini, Groq) and frameworks (LangGraph, LangChain) indicates a willingness to explore and integrate different tools. The candidate is currently pursuing a B.Tech in Computer Science, indicating a proactive learning mindset. The remote internship experience also suggests an ability to work independently.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to design and implement complex AI systems, suggesting strong problem-solving and analytical skills. The focus on modularity, state management, and structured outputs points to an organized and detail-oriented approach. The visualization of agent reasoning in one project suggests an appreciation for transparency and debugging in complex systems. However, without direct interaction or psychometric test results, a comprehensive assessment of soft skills like teamwork, communication, and stress handling is limited.