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AI Engineer with less than a year in RAG & Cloud-based AI applications.
AI / GenAI Engineer with hands-on experience building enterprise-grade Retrieval-Augmented Generation (RAG) and Autonomous AI Agent systems using LangChain and LangGraph. Experienced in designing document intelligence pipelines, deploying ML workflows, and building scalable cloud-based AI applications. Skilled in Python, LLMs, vector embeddings, FAISS, Docker, and AWS, with a strong focus on document-grounded reasoning, hallucination reduction, and production-style GenAI architectures. Seeking AI / GenAI Engineer roles to build and deploy real-world intelligent systems.
G.H. Raisoni College Of Engineering
Bachelor of Technology · Artificial Intelligence And Machine Learning
June 1, 2021 – July 1, 2024
AI Adventures
Data Analyst Intern
July 1, 2024 – January 1, 2025
Pune, Maharashtra, India
Autonomous AI Agent for Document Intelligence (RAG)
June 24, 2026 – Present
Built an autonomous AI agent using LangGraph with LangChain-based RAG components for document-grounded reasoning. Designed an end-to-end document intelligence pipeline using LangChain, HuggingFace embeddings, and FAISS vector search for semantic retrieval. Integrated Groq LLAMA 3.1 for low-latency LLM inference, ensuring responses are generated strictly from retrieved document context to reduce hallucinations. Architected a modular, extensible GenAI system aligned with enterprise use cases such as knowledge assistants, compliance Q&A, and research workflows.
View ProjectEnterprise Document Q&A System (Multi-Level RAG)
June 24, 2026 – Present
Designed and implemented a multi-level, enterprise-grade Retrieval-Augmented Generation (RAG) architecture for document question answering. Built a structured document ingestion pipeline using LlamaParse to extract text, tables, and metadata, followed by JSON-based chunking and normalization. Implemented high-quality vector embeddings using MixedBread with a persistent vector store to enable fast, reusable semantic retrieval across sessions. Engineered context-aware retrieval using LlamaIndex query engines and integrated Groq LLM to generate responses strictly grounded in retrieved context. Containerized the end-to-end pipeline using Docker and architected the system for future FastAPI-based backend services and production deployment.
View ProjectGlobal Population Analysis on AWS
June 24, 2026 – Present
Designed and deployed an ETL data pipeline using AWS S3, Glue, Lambda, and Athena for large-scale population data analysis. Automated data ingestion and transformation, generating analytical insights through SQL and QuickSight dashboards. Demonstrated end-to-end data workflow management - from raw ingestion to visualization for global trends.
View ProjectIMDB Movie Rating Prediction using Machine Learning
June 24, 2026 – Present
Built an ML model using Python (Pandas, NumPy, Scikit-learn) to predict IMDb ratings via Linear Regression and Random Forest. Enhanced model performance through feature engineering and hyperparameter tuning (GridSearchCV), achieving R²: 0.83. Visualized results using Matplotlib and Seaborn to identify trends and evaluate model accuracy.
View ProjectDesigning and Implementing Big Data Analytics Solutions
Unknown
June 12, 2026 – Present
Data Engineering on AWS - Foundations
Unknown
June 12, 2026 – Present
AWS Cloud Practitioner Essentials
Unknown
June 12, 2026 – Present
IBM TechXchange Dev Day - Virtual Agents (Certificate of Participation)
IBM
June 12, 2026 – Present
Cultural Fit Analysis
The candidate's projects demonstrate a strong interest and practical application in AI/GenAI, aligning well with an AI Engineer role. The diversity of projects, from RAG systems to AWS data pipelines and traditional ML, shows a broad technical curiosity. The academic background in AI/ML further reinforces this fit. The candidate appears to be a proactive learner, evidenced by multiple certifications and self-initiated projects.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to design end-to-end systems and collaborate (as mentioned in the intern role). The focus on enterprise-grade solutions and modular architectures suggests an understanding of operational requirements. However, direct evidence of soft skills like leadership, conflict resolution, or advanced teamwork is limited to project descriptions.