I’m a Data Engineer with 6+ years of professional experience, currently working at Entual GmbH in Germany. My professional foundation is in Ab Initio and enterprise-scale data engineering, and my work has expanded into distributed systems, runtime behaviour, GPU computing, local AI, and performance-oriented software.

I’m especially interested in systems where data movement, execution models, memory, and hardware constraints directly shape performance and reliability.

Portfolio · LinkedIn

Experimental runtime work focused on translating CUDA-oriented workloads toward Apple Metal, exploring runtime compatibility, execution models, and GPU portability on Apple Silicon.

C/C++ · CUDA · Metal · Apple Silicon · Runtime Systems · GPU Computing

A systems-focused project exploring local execution, tooling, and resource-aware compute workflows with an emphasis on practical infrastructure and performance-conscious design.

Rust · Systems Programming · Local Compute · Performance Engineering

My active development branch on llama.cpp, with work around persistent and file-backed KV cache, mmap-based memory optimisation, Vulkan/runtime improvements, GPU-backed cache behaviour, and server extensions.

C/C++ · LLM Inference · KV Cache · mmap · Vulkan · GPU Computing

Ab Initio and Data Engineering have been the core of my professional engineering career.

I have worked with enterprise-scale data-processing environments where correctness, reliability, scalability, recoverability, operational stability, and performance are critical.

Key areas include:

Ab Initio · GDE · Conduct>It · Control Center · Express>It · TRW

ETL / ELT · Data Integration · Data Quality · Batch Processing · Production Support · Performance Tuning

Broader data-engineering tools and technologies:

SQL · Oracle · PostgreSQL · Python · Shell / KornShell

I completed an M.Sc. in Data Science, which broadened that engineering foundation into machine learning, research workflows, and data-intensive systems.

My current interests sit at the intersection of data systems and lower-level compute:

• distributed and parallel data processing
• runtime and memory behaviour
• systems programming in Rust and C/C++
• GPU computing and hardware-aware optimisation
• local and resource-efficient AI
• LLM inference and cache architecture
• performance engineering across software and hardware boundaries

A recurring question in my work is: where is the real bottleneck, and what layer is actually responsible for it?

Data Engineering

Ab Initio · ETL / ELT · SQL · Oracle · PostgreSQL · Data Quality

Programming & Systems

Python · Rust · C/C++ · Shell · KornShell

Distributed & Parallel Systems

Kafka · Airflow · Ray · AsyncIO · Parallel Processing

Compute & AI Infrastructure

CUDA · Metal · Vulkan · Apple Silicon · LLM Inference · Local AI

Platforms

Linux · macOS

For a more complete view of my engineering work, projects, and background:

• Portfolio: https://perinban.github.io/portfolio/
• GitHub: https://github.com/Perinban
• LinkedIn: https://www.linkedin.com/in/perinban-parameshwaran/