
AI Engineer with less than a year in AI/ML & RAG-based systems.
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Vaibhav Dubey is an aspiring AI Engineer with 0.4 years of internship experience at Zlice, focusing on AI workflow automation, vector search, and prompt orchestration. His technical prowess spans AI Engineering, Machine Learning, LLM & AI Platforms, and various databases. He has led impactful projects, including developing an Autonomous Enterprise Manager, a Hybrid AI Recruitment Platform, and an RUL prediction framework, demonstrating strong skills in system architecture, data processing, and model development.
National Institute of Technology, Kurukshetra
B.Tech · Electrical Engineering
August 1, 2022 – June 30, 2026
Zlice
AI Intern
January 1, 2026 – May 1, 2026
India
Hybrid AI Recruitment Platform
January 1, 2024 – June 1, 2026
• Architected a scalable AI recruitment engine for semantic candidate search, ranking, recommendation, and explainable hiring decisions across 100,000+ candidate profiles. • Engineered a hybrid retrieval and Learning-to-Rank pipeline that improved retrieval-signal detection from 21.6% to 43.0% while increasing ranking quality across enterprise hiring datasets. • Designed automated evaluation and quality auditing pipelines achieving 82% retrieval coverage, 66% ranking coverage, and 56% recommendation coverage within Top-100 candidate recommendations. • Optimized end-to-end candidate processing to rank 100K profiles in ~26 seconds while generating explainable recommendations and validated hiring reports.
View ProjectAutonomous Enterprise Manager
January 1, 2024 – June 1, 2026
• Architected a modular Enterprise AI Operating System with layered knowledge, memory, orchestration, capability, governance, and observability architectures for scalable multi-agent enterprise automation. • Built a cognitive knowledge platform capable of ingesting enterprise documents and GitHub repositories, performing semantic retrieval, long-term memory reasoning, and grounded question answering with 94% grounded-answer rate, 89% citation accuracy, and 85% Recall@5. • Designed a three-layer cognitive memory architecture combining episodic, semantic, and structured memory with asynchronous extraction, importance scoring, and deduplication, achieving 90% memory precision, 92% deduplication efficiency, and 88% prompt token reduction. • Developed a benchmarking and evaluation platform for enterprise AI systems, validating multi-agent workflows over 50 benchmark iterations and achieving 64.7 ms retrieval latency, 2.23 s end-to-end latency, and 92.5% workflow success.
View ProjectEarly Prediction of Lithium-Ion Battery Remaining Useful Life
January 1, 2024 – June 1, 2026
• Developed an intelligent prognostics framework for early Remaining Useful Life (RUL) estimation using NASA battery degradation datasets and advanced feature engineering. • Engineered degradation-aware feature extraction pipelines capturing capacity fade, State-of-Health trends, and temporal degradation patterns for robust predictive modeling. • Built and benchmarked multiple machine learning and deep learning models, achieving a best Test MAE of 51.63 while extending the framework to LSTM and Transformer architectures for long-horizon prediction.
View ProjectCultural Fit Analysis
The candidate's projects demonstrate a strong interest and capability in cutting-edge AI domains, particularly multi-agent systems, semantic search, and AI evaluation, which aligns well with an AI Engineer role. The diversity of projects (enterprise automation, recruitment, battery prognostics) shows a broad application of AI skills. The academic background in Electrical Engineering combined with self-driven AI projects indicates a proactive and continuous learning mindset. The candidate's experience level is entry-level, but the depth of personal projects suggests a high potential for growth and contribution.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a strong ability to work on complex, multi-faceted problems, suggesting good problem-solving and analytical skills. The detailed metrics provided for project outcomes imply a results-oriented approach. The experience with GitHub Actions and CI/CD suggests an understanding of operational best practices. However, direct evidence of teamwork, leadership, or communication in a collaborative setting is limited to project descriptions.