Your role and responsibilities

A hands-on engineering position responsible for designing, automating, and maintaining robust build systems and deployment pipelines for AI/ML components, with direct development responsibilities in C++ and Python. The role supports both model training infrastructure and high-performance inference systems.

1. Design and implement robust build automation systems that support large, distributed AI/C++/Python codebases.
2. Develop tools and scripts that enable developers and researchers to rapidly iterate, test, and deploy across diverse environments.
3. Integrate C++ components with Python-based AI workflows, ensuring compatibility, performance, and maintainability.
4. Lead the creation of portable, reproducible development environments, ensuring parity between development and production.
5. Maintain and extend CI/CD pipelines for Linux and z/OS, implementing best practices in automated testing, artifact management, and release validation.
6. Collaborate with cross-functional teams — including AI researchers, system architects, and mainframe engineers — to align infrastructure with strategic goals.
7. Proactively monitor and improve build performance, automation coverage, and system reliability.
8. Contribute to internal documentation, process improvements, and knowledge sharing to scale your impact across teams

Required education

Bachelor's Degree

Preferred education

Bachelor's Degree

Required technical and professional expertise

1. With 5+ years of strong programming skills in C++ and Python, with a deep understanding of both compiled and interpreted language paradigms.
2. Hands-on experience building and maintaining complex automation pipelines (CI/CD) using tools like Jenkins, or GitLab CI.
3. In-depth experience with build tools and systems such as CMake, Make, Meson, or Ninja, including custom script development and cross-compilation.
4. Experience working on multi-platform development, specifically on Linux and IBM z/OS environments, including understanding of their respective toolchains and constraints.
5. Experience integrating native C++ code with Python, leveraging pybind11, Cython, or similar tools for high-performance interoperability.
6. Proven ability to troubleshoot and resolve build-time, runtime, and integration issues in large-scale, multi-component systems.
7. Comfortable with shell scripting (Bash, Zsh, etc.) and system-level operations.
8. Familiarity with containerization technologies like Docker for development and deployment environments.