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AI Engineer with less than a year in Generative AI & Machine Learning.
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Assessing your cultural and operational fit
Aspiring Data Scientist and ML/AI Engineer with solid academic training and hands-on experience engineering end-to-end intelligent systems. Proven capability in developing production-ready Generative AI solutions, including Multi-Agent Retrieval-Augmented Generation (RAG) platforms using LangGraph, and modular machine learning pipelines. Competent in designing scalable ML pipelines, hybrid search engines, and containerized deployments using Docker. Seeking to apply software engineering best practices and advanced machine learning techniques in a dynamic engineering team.
Silicon University
B.Tech · Computer Science and Technology
August 1, 2022 – June 30, 2026
Dhanbad Public School
Senior Secondary · Class XII
June 1, 2020 – May 31, 2022
Dhanbad Public School
Secondary · Class X
June 1, 2018 – May 31, 2020
Enterprise Multi-Agent RAG Platform
June 1, 2026 – Present
Engineered an enterprise-grade, local Multi-Agent RAG platform using LangGraph to coordinate retriever, reranker, generator, and citation agents. Implemented a high-performance Hybrid Search engine combining BM25 keyword matching and ChromaDB vector search (Nomic-Embed-Text via Ollama), fused with custom weighted scoring. Integrated a Cross-Encoder Reranker (sentence-transformers/ms-marco-MiniLM-L-6-v2) to re-score top documents, reducing LLM hallucination and enhancing contextual precision. Built an automated citation mapping agent to dynamically inject inline source anchors, linking generated answers directly back to specific document segments. Developed a live Evaluation Dashboard using FastAPI, SQLAlchemy, and SQLite to log queries, capture user thumbs-up/down feedback, and track performance metrics (latency, citation density). Containerized the platform using Docker and Docker Compose for seamless local deployment.
Network Security Phishing Detection
April 1, 2025 – April 1, 2026
Built a modular, production-ready machine learning pipeline for phishing website detection, achieving a classification accuracy of 95%. Developed robust data ingestion, validation, and preprocessing components, storing raw network data and validation schemas in MongoDB. Engineered advanced feature extraction pipelines using Scikit-Learn and NLP techniques to preprocess URLs and metadata. Leveraged MLflow and DagsHub to track experiments, manage model runs, and register optimized classifiers (XGBoost, Random Forest). Created RESTful APIs using FastAPI to serve model predictions asynchronously, and designed an interactive HTML web interface for batch CSV predictions.
Training in Data Science, Machine Learning, and Deep Learning using Python
Syllogistek pvt ltd
June 1, 2026 – Present
Python Programming Certification
NPTEL
June 1, 2026 – Present
Core Java Developer Certification
Silan Software
June 1, 2026 – Present
Complete Generative AI course with huggingface and langchain
Udemy
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's projects showcase a strong interest and practical application in cutting-edge AI/ML technologies, aligning well with an AI Engineer role. The diversity of projects, from network security to enterprise RAG platforms, indicates a broad technical curiosity and ability to adapt to different problem domains. The academic background combined with self-driven certifications suggests a proactive learning attitude. However, the lack of professional experience might require mentorship in a fast-paced corporate environment.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through complex project implementations. The detailed descriptions of project architectures and technical choices suggest an analytical mindset and a structured approach to engineering. The use of MLOps tools indicates an understanding of operationalizing ML systems. However, without direct experience or behavioral assessment, specific soft skills like teamwork, leadership, or adaptability cannot be fully evaluated.