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AI Engineer with less than a year in machine learning model deployment and data analytics.
Parminder Singh is an aspiring AI Engineer with an Integrated Dual Degree in Computer Science Engineering & Artificial Intelligence. He has hands-on experience in developing, optimizing, and deploying machine learning models on embedded platforms for edge AI applications. His project work includes building a production-grade ML recommendation system, an AI document intelligence system with RAG, and a data-driven climate analytics platform. With strong skills in Python, ML frameworks, and cloud deployment, Parminder is a keen open-source contributor in the AI/ML domain.
Rajiv Gandhi Institute of Petroleum Technology
Integrated Dual Degree · Computer Science Engineering & Artificial Intelligence
August 1, 2022 – June 30, 2027
MGM Public School
Higher Secondary Education
N/A – May 31, 2021
Shri Harkrishan Sahib Public High School
Matriculation
N/A – May 31, 2019
Hypertangent Technologies- IIT Delhi
Machine Learning Intern
May 1, 2025 – July 1, 2025
Delhi, Delhi, India
Atlas
June 20, 2026 – Present
Built and deployed a production-grade ML recommendation system using a two-stage pipeline (candidate generation + learning-to-rank), trained on 2.7M real-world e-commerce interactions. Implemented collaborative filtering, item similarity, popularity fallback, and session-aware reranking to handle cold-start users and sparse interaction data. Deployed scalable microservices across Azure AKS and Render with deployment-optimized ML inference, Neon PostgreSQL integration, and containerized infrastructure.
View ProjectSpectralReader
June 20, 2026 – Present
Built an AI document intelligence system performing OCR, semantic chunking, and embedding-based retrieval for summarization and QA over large PDF corpora. Achieved high query relevancy (~92%) with ~1.2 s response latency using transformer-based RAG with FAISS retrieval.
View ProjectWeatherScope
June 20, 2026 – Present
Designed a data-driven climate analytics platform integrating NASA MERRA-2, IMERG, POWER, and Earthdata APIs with asynchronous multi-source pipelines. Achieved sub-3 s global-query latency; deployed microservices with a 3D WebGL climate visualization dashboard.
View ProjectCultural Fit Analysis
The candidate's project portfolio showcases a strong interest in AI and data-driven solutions, aligning well with an AI Engineer role. The diversity of projects (recommendation systems, document intelligence, climate analytics) indicates a broad curiosity and willingness to tackle different problem domains. Open-source contributions further highlight a collaborative mindset. However, the lack of professional experience beyond a single internship, and that being in the future, means cultural fit is primarily inferred from personal initiative and project work, which might require further validation.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills and a proactive approach through their diverse personal projects and achievements (NASA Space Apps Challenge, Open Source contributions). Their ability to work with complex data sources and deploy scalable solutions indicates good operational fit for roles requiring robust system development. The detailed project descriptions suggest good technical communication, though direct interaction would be needed to fully assess collaboration and stress handling.