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Presentation
I am a Senior AI & Machine Learning Engineer with a PhD in Artificial Intelligence, specializing in bridging the gap between cutting-edge research and production-grade systems.
Core Expertise:
Enterprise RAG & Agentic AI: Architecting multi-stage retrieval pipelines with autonomous self-correction.
Hardware-Specific Optimization: Expert in optimizing deep learning models (PyTorch/MLX) for Apple Silicon (M-series) using Metal Performance Shaders and Unified Memory architectures.
Medical Computer Vision: Extensive experience in self-supervised learning for histopathology and automated cellular screening.
Generative Engines: Developer of "Project Aether," a natural language-to-2D game engine using custom DSLs.
I provide high-performance, cost-effective AI solutions for startups and enterprises looking for robust, scalable, and hardware-aware intelligence.
Core Expertise:
Enterprise RAG & Agentic AI: Architecting multi-stage retrieval pipelines with autonomous self-correction.
Hardware-Specific Optimization: Expert in optimizing deep learning models (PyTorch/MLX) for Apple Silicon (M-series) using Metal Performance Shaders and Unified Memory architectures.
Medical Computer Vision: Extensive experience in self-supervised learning for histopathology and automated cellular screening.
Generative Engines: Developer of "Project Aether," a natural language-to-2D game engine using custom DSLs.
I provide high-performance, cost-effective AI solutions for startups and enterprises looking for robust, scalable, and hardware-aware intelligence.
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Background
Senior AI & Machine Learning Engineer
Architected and deployed production-grade Enterprise RAG engines, utilizing multi-stage retrieval pipelines and agentic self-correction to ensure 99% accuracy in information retrieval.
Optimized deep learning training and inference for Apple Silicon (M2 Pro/M3), leveraging Metal Performance Shaders (MPS) and Unified Memory architectures to reduce latency by 12%.
Engineered environment-based secret management and security protocols for enterprise-scale codebases, resolving critical vulnerabilities in collaborative repositories.
Lead Developer of "Project Aether": Built a generative game engine that converts natural language into functional 2D games via a custom JSON Domain-Specific Language (DSL).
Optimized deep learning training and inference for Apple Silicon (M2 Pro/M3), leveraging Metal Performance Shaders (MPS) and Unified Memory architectures to reduce latency by 12%.
Engineered environment-based secret management and security protocols for enterprise-scale codebases, resolving critical vulnerabilities in collaborative repositories.
Lead Developer of "Project Aether": Built a generative game engine that converts natural language into functional 2D games via a custom JSON Domain-Specific Language (DSL).
Artificial Intelligence
Cost Reduction: I don't just "run" models; I architect them to be computationally efficient, reducing cloud/hardware overhead.
High Reliability: My research background allows me to implement rigorous verification and agentic "checks" to ensure AI outputs are production-safe.
Custom Tooling: Expertise in building custom Domain-Specific Languages (DSLs) that allow non-technical users to interact with complex AI systems.
High Reliability: My research background allows me to implement rigorous verification and agentic "checks" to ensure AI outputs are production-safe.
Custom Tooling: Expertise in building custom Domain-Specific Languages (DSLs) that allow non-technical users to interact with complex AI systems.
Msc Computer Engineering
Hardware Acceleration: Deep understanding of FPGA/ASIC design patterns for neural network inference.
Parallel Architectures: Mastery of SIMD (Single Instruction, Multiple Data) and multi-core synchronization for high-throughput AI workloads.
Edge AI & Embedded Intelligence: Experience in Model Compression (Quantization and Pruning) to fit complex architectures onto resource-constrained embedded systems.
Heterogeneous Computing: Expertise in partitioning AI workloads between the CPU, GPU, and specialized NPU (Neural Processing Unit) to optimize for low power and low latency.
Parallel Architectures: Mastery of SIMD (Single Instruction, Multiple Data) and multi-core synchronization for high-throughput AI workloads.
Edge AI & Embedded Intelligence: Experience in Model Compression (Quantization and Pruning) to fit complex architectures onto resource-constrained embedded systems.
Heterogeneous Computing: Expertise in partitioning AI workloads between the CPU, GPU, and specialized NPU (Neural Processing Unit) to optimize for low power and low latency.
Computer Engineering
Hardware: VLSI Design, Embedded Systems, and FPGA Programming.
Systems: Real-Time Operating Systems (RTOS) and Network Protocol design.
Mathematics: Discrete Structures, Linear Algebra, and Calculus (the bedrock of Machine Learning).
Software: Data Structures & Algorithms, Compilers, and Advanced C++/Assembly.
Systems: Real-Time Operating Systems (RTOS) and Network Protocol design.
Mathematics: Discrete Structures, Linear Algebra, and Calculus (the bedrock of Machine Learning).
Software: Data Structures & Algorithms, Compilers, and Advanced C++/Assembly.
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Working hours
Monday â–ª Today
From08h00To18h00
Tuesday
From08h00To18h00
Wednesday
From08h00To18h00
Thursday
From08h00To18h00
Friday
From08h00To18h00
Saturday
Closed
Sunday
Closed
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