Job
Description
A hands-on engineering position responsibe for designing, automating, and maintaining robust buid systems and depoyment pipeines for AI/ML components, with direct deveopment responsibiities in C++ and Python. The roe supports both mode training infrastructure and high-performance inference systems. Design and impement robustbuid automation systemsthat support arge, distributed AI/C++/Python codebases. Deveop toos and scripts that enabe deveopers and researchers to rapidy iterate, test, and depoy across diverse environments. Integrate C++ components with Python-based AI workfows, ensuring compatibiity, performance, and maintainabiity. Lead the creation ofportabe, reproducibe deveopment environments, ensuring parity between deveopment and production. Maintain and extend CI/CD pipeines for Linux and z/OS, impementing best practices in automated testing, artifact management, and reease vaidation. Coaborate with cross-functiona teams incuding AI researchers, system architects, and mainframe engineers to aign infrastructure with strategic goas. Proactivey monitor and improve buid performance, automation coverage, and system reiabiity. Contribute to interna documentation, process improvements, and knowedge sharing to scae your impact across teams. Required education Bacheor's Degree Preferred education Bacheor's Degree Required technica and professiona expertise Strong programming skis in C++ and Python, with a deep understanding of both compied and interpreted anguage paradigms. Hands-on experience buiding and maintainingcompex automation pipeines(CI/CD) using toos ikeJenkins, or GitLab CI. In-depth experience withbuid toos and systemssuch asCMake, Make, Meson, or Ninja, incuding custom script deveopment and cross-compiation. Experience working onmuti-patform deveopment, specificay onLinux and IBM z/OSenvironments, incuding understanding of their respective toochains and constraints. Experience integratingnative C++ code with Python, everagingpybind11,Cython, or simiar toos for high-performance interoperabiity. Proven abiity to troubeshoot and resovebuid-time, runtime, and integration issuesin arge-scae, muti-component systems. Comfortabe withshe scripting(Bash, Zsh, etc.) and system-eve operations. Famiiarity withcontainerization technoogiesike Docker for deveopment and depoyment environments. Preferred technica and professiona experience Working knowedge of AI/ML frameworks such as PyTorch, TensorFow, or ONNX, incuding understanding of how they integrate into production environments. Experience deveoping or maintaining software on IBM z/OS mainframe systems. Famiiarity with z/OS buid and packaging workfows, Understanding of system performance tuning, especiay in high-throughput compute or I/O environments (e.g., arge mode training or inference). Knowedge of GPU computing and ow-eve profiing/debugging toos. Experience managing ong-ifecyce enterprise systems and ensuring compatibiity across reeases and depoyments. Background contributing to or maintaining open-source projects in the infrastructure, DevOps, or AI tooing space Proficiency in distributed systems, microservice architecture, and REST APIs. Experience in coaborating with cross-functiona teams to integrate MLOps pipeines with CI/CD toos for continuous integration and depoyment, ensuring seamess integration of AI/ML modes into production workfows. Strong communication skis with the abiity to communicate technica concepts effectivey to non-technica stakehoders. Demonstrated exceence in interpersona skis, fostering coaboration across diverse teams. Proven track record of ensuring compiance with industry best practices and standards in AI engineering. Maintained high standards of code quaity, performance, and security in AI projects.
