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MLOps Engineer with less than a year in MLOps and GenAI
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Results-driven AI/ML Engineer and Data Scientist with hands-on experience building production-grade machine learning systems, LLM-powered applications, and end-to-end MLOps pipelines. Skilled in deploying scalable Al infrastructure using Docker, Kubernetes, and AWS with CI/CD automation. Proficient in Retrieval-Augmented Generation (RAG), computer vision, and multi-level classification systems. Passionate about bridging cutting-edge Al research and real-world deployment to deliver measurable business impact.
Shri Vishwakarma Skill University
B.Tech · Artificial Intelligence & Machine Learning
August 1, 2022 – June 30, 2026
Swami Vivekanand Public School
12th Grade - PCM · Physics, Chemistry, Mathematics
June 1, 2020 – May 31, 2021
Swami Vivekanand Public School
10th Grade - CBSE
June 1, 2018 – May 31, 2019
Zenith India
AI/ML Engineer Intern
February 1, 2026 – Present
Jaipur, Rajasthan, India
Hybrid RAG Pipeline with Qwen, FAISS & XGBoost
June 24, 2026 – Present
Built an advanced Hybrid Retrieval-Augmented Generation (RAG) pipeline combining dense vector search (FAISS), intelligent re-ranking (XGBoost), and high-quality generation (Qwen LLM) - achieving superior context accuracy over naive RAG approaches. Implemented FAISS for fast, scalable similarity search across large document embedding spaces; integrated XGBoost re-ranker to score retrieved chunks on deeper semantic features before passing to the generator. Leveraged the Qwen LLM to synthesize accurate, hallucination-minimised responses from re-ranked context, significantly improving answer relevance and factual grounding. Demonstrated expertise in combining classical ML (XGBoost) with modern LLM architectures in a unified production-ready pipeline.
View ProjectVehicle Insurance Prediction – End-to-End MLOps Pipeline
June 24, 2026 – Present
Built an enterprise-grade, end-to-end ML solution predicting customer vehicle insurance purchase intent, covering full MLOps lifecycle: data ingestion from MongoDB, validation, transformation, model training, and deployment. Trained and evaluated an XGBoost classifier with automated data validation and transformation steps to ensure consistent, high-quality inputs throughout the pipeline. Implemented GitHub Actions CI/CD for automated testing and continuous deployment, and containerised the full application with Docker for environment consistency. Deployed a FastAPI inference endpoint on AWS, enabling scalable real-time predictions accessible via REST API - demonstrating production-level MLOps maturity.
View ProjectReal-Time Footfall Counter – Computer Vision System
June 24, 2026 – Present
Developed a robust real-time computer vision system to count people entering and exiting an area using YOLO-based object detection paired with SORT/DeepSORT multi-object tracking algorithms. Assigned unique persistent IDs to each detected individual across consecutive frames; calculated bounding box centroids to determine IN/OUT movement direction relative to a configurable virtual reference line. Engineered the system to process both pre-recorded video files and live RTSP camera streams, outputting real-time on-screen analytics - applicable for retail, security, and smart building use cases.
View ProjectCultural Fit Analysis
The candidate's projects showcase a diverse range of applications, from RAG pipelines and computer vision to MLOps infrastructure, indicating adaptability and a broad interest in AI/ML domains. The internship at Zenith India, focusing on educational apps and toys, suggests an interest in impactful applications. The personal projects demonstrate initiative and self-driven learning, which are positive indicators for cultural fit in a dynamic environment. The target role of MLOps Engineer aligns well with the candidate's demonstrated skills in deploying and managing ML systems.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through complex project architectures (hierarchical classification, hybrid RAG). The emphasis on 'production-ready' and 'enterprise-grade' solutions, along with CI/CD and containerization, indicates a good operational fit for MLOps roles. The focus on 'hallucination-free output' and 'medical authenticity' in the mental health app project highlights attention to detail and responsibility, crucial for sensitive applications.