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AI Engineer with 1+ years in RAG, Vector Search & LLM Agents
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Assessing your cultural and operational fit
Software engineer with 1.5+ years at Oracle on the APEX Workflow Engine, shipping production features and building AI systems - RAG pipelines, LLM-powered document processing, and agent memory design. Completing an engineering degree in AI & Big Data (Sep 2026).
ISGA - Edvantis
Engineering Cycle · AI & Big Data
N/A – September 1, 2026
Multidisciplinary Faculty of Beni Mellal, Morocco
Bachelor · Mathematics & Computer Science (SMI)
N/A – July 1, 2024
Oracle
Software Engineer
November 1, 2024 – May 1, 2026
India
Oracle Labs PGX
Research Intern
July 1, 2024 – October 1, 2024
India
AutoNoSQL: ML-Driven Query Optimizer for MongoDB and Cassandra
June 29, 2026 – Present
Designed and built a tool that automatically analyzes database queries and recommends fixes for slow ones. A trained classifier predicts query quality upfront and only triggers LLM when optimization is needed — cutting expensive AI processing roughly in half
Loan Approval Prediction Pipeline
June 29, 2026 – Present
Built an end-to-end ML pipeline streaming loan data through Kafka and MongoDB into a distributed Spark training workflow. Trained and benchmarked three classification models, automating best-model selection and delivering segmented risk insights to Power BI dashboards.
Cultural Fit Analysis
The candidate's project diversity, ranging from academic research in graph algorithms to production-level AI system development at Oracle, indicates a broad interest and adaptability. Their involvement in both core engineering tasks and advanced AI/ML projects suggests a versatile profile. The target role of AI Engineer aligns perfectly with their demonstrated skills and experience in RAG, LLMs, and data processing. The breadth of technologies used across projects (Python, Spark ML, Kafka, MongoDB, Pinecone, various LLMs) further supports a strong cultural fit for a dynamic AI engineering environment.
Soft Skills & Operational Fit
The candidate's resume demonstrates strong problem-solving skills through the design and implementation of complex AI solutions. Their experience in optimizing AI retrieval accuracy and building end-to-end pipelines suggests a methodical and results-oriented approach. The descriptions of their contributions to conversational assistants and multi-tenancy support indicate good collaboration and adaptability to team requests. The academic projects also highlight initiative and the ability to tackle challenging technical problems independently.