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I am a Master’s student in Computer Science (Artificial Intelligence) with strong foundations in machine learning, hybrid AI, knowledge graphs, and distributed systems. I have experience across academic research, industrial AI internships, and system-level engineering, and I enjoy bridging theory and practice across AI, IoT, and software systems. My master’s thesis on Hybrid AI for IoT explores combining ML with symbolic reasoning using RDF knowledge graphs and proposes a SPARQL extension to execute ML models during query evaluation. I implemented and evaluated it using the Kolibrie SPARQL engine with cost-based query planning.…I am a Master’s student in Computer Science (Artificial Intelligence) with strong foundations in machine learning, hybrid AI, knowledge graphs, and distributed systems. I have experience across academic research, industrial AI internships, and system-level engineering, and I enjoy bridging theory and practice across AI, IoT, and software systems. My master’s thesis on Hybrid AI for IoT explores combining ML with symbolic reasoning using RDF knowledge graphs and proposes a SPARQL extension to execute ML models during query evaluation. I implemented and evaluated it using the Kolibrie SPARQL engine with cost-based query planning.

Srinjoy Sanyal

Data Scientist, AI Engineer, Full Stack Developer, +2

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I am a Master’s student in Computer Science (Artificial Intelligence) with strong foundations in machine learning, hybrid AI, knowledge graphs, and distributed systems. I have experience across academic research, industrial AI internships, and system-level engineering, and I enjoy bridging theory and practice across AI, IoT, and software systems. My master’s thesis on Hybrid AI for IoT explores combining ML with symbolic reasoning using RDF knowledge graphs and proposes a SPARQL extension to execute ML models during query evaluation. I implemented and evaluated it using the Kolibrie SPARQL engine with cost-based query planning.…I am a Master’s student in Computer Science (Artificial Intelligence) with strong foundations in machine learning, hybrid AI, knowledge graphs, and distributed systems. I have experience across academic research, industrial AI internships, and system-level engineering, and I enjoy bridging theory and practice across AI, IoT, and software systems. My master’s thesis on Hybrid AI for IoT explores combining ML with symbolic reasoning using RDF knowledge graphs and proposes a SPARQL extension to execute ML models during query evaluation. I implemented and evaluated it using the Kolibrie SPARQL engine with cost-based query planning.

Available to hire

I am a Master’s student in Computer Science (Artificial Intelligence) with strong foundations in machine learning, hybrid AI, knowledge graphs, and distributed systems. I have experience across academic research, industrial AI internships, and system-level engineering, and I enjoy bridging theory and practice across AI, IoT, and software systems.

My master’s thesis on Hybrid AI for IoT explores combining ML with symbolic reasoning using RDF knowledge graphs and proposes a SPARQL extension to execute ML models during query evaluation. I implemented and evaluated it using the Kolibrie SPARQL engine with cost-based query planning.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Intermediate

Intermediate

Intermediate

Intermediate

Language

English

Fluent

French

Fluent

Dutch

Beginner

Latin

Beginner

Work Experience

AI Internship at Proximus / Codit

June 1, 2024 - August 31, 2024

Extended GenAI PoC templates deployed on Microsoft Azure; Developed reusable pipelines and MLOps / GenAIOps processes; Benchmarked LLM deployment using vLLM and Skypilot; Migrated cloud-based AI pipelines to on-premise GPU environments.

SystemC Internship at NXP Semiconductors

June 1, 2025 - August 31, 2025

Developed SystemC models for complex digital ICs; Integrated ARM Cortex Fast Models with RAM and peripherals; Built and simulated C applications interacting with hardware registers.