Build and Automation Engineer

IBM Systems helps IT leaders think differently about their infrastructure. IBM servers and storage are no longer inanimate - they can understand, reason, and learn so our clients can innovate while avoiding IT issues. Our systems power the worlds most important industries and our clients are the architects of the future. Join us to help build our leading-edge technology portfolio designed for cognitive business and optimized for cloud computing.

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 robustbuild automation systemsthat 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 ofportable, 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.

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 maintainingcomplex automation pipelines(CI/CD) using tools likeJenkins, or GitLab CI.
3. In-depth experience withbuild tools and systemssuch asCMake, Make, Meson, or Ninja, including custom script development and cross-compilation.
4. Experience working onmulti-platform development, specifically onLinux and IBM z/OSenvironments, including understanding of their respective toolchains and constraints.
5. Experience integratingnative C++ code with Python, leveragingpybind11,Cython, or similar tools for high-performance interoperability.
6. Proven ability to troubleshoot and resolvebuild-time, runtime, and integration issuesin large-scale, multi-component systems.
7. Comfortable withshell scripting(Bash, Zsh, etc.) and system-level operations.
8. Familiarity withcontainerization technologieslike Docker for development and deployment environments.

1. Working knowledge of AI/ML frameworks such as PyTorch, TensorFlow, or ONNX, including understanding of how they integrate into production environments.
2. Experience developing or maintaining software on IBM z/OS mainframe systems.
3. Familiarity with z/OS build and packaging workflows,
4. Understanding of system performance tuning, especially in high-throughput compute or I/O environments (e.g., large model training or inference).
5. Knowledge of GPU computing and low-level profiling/debugging tools.
6. Experience managing long-lifecycle enterprise systems and ensuring compatibility across releases and deployments.
7. Background contributing to or maintaining open-source projects in the infrastructure, DevOps, or AI tooling space
8. Proficiency in distributed systems, microservice architecture, and REST APIs.
9. Experience in collaborating with cross-functional teams to integrate MLOps pipelines with CI/CD tools for continuous integration and deployment, ensuring seamless integration of AI/ML models into production workflows.
10. Strong communication skills with the ability to communicate technical concepts effectively to non-technical stakeholders.
11. Demonstrated excellence in interpersonal skills, fostering collaboration across diverse teams.
12. Proven track record of ensuring compliance with industry best practices and standards in AI engineering.
13. Maintained high standards of code quality, performance, and security in AI projects.