Principal Machine Learning Engineer
We are seeking GCP Machine Learning Engineers (MLE) with strong experience deploying machine learning solutions into production using state‑of‑the‑art tools and following DevOps and test‑driven development practices. MLEs work closely with data scientists and data engineers to focus on model performance, delivery stability, reproducibility, and scalability.
Essential Functions
* Collaborate with Data Scientists to test and scale new algorithms through pilots and industrialize solutions at scale.
* Influence, build and maintain the large‑scale data infrastructure required for AI projects and integrate with external IT infrastructure/services to provide end‑to‑end solutions.
* Leverage software architecture and design patterns to write scalable, maintainable, future‑proof code.
* Design, develop, and maintain the framework for analytical pipelines.
* Develop common components to address pain points in machine learning projects, such as model lifecycle management, feature store, and data quality evaluation.
* Provide input and help implement framework and tools to improve data quality.
* Work in cross‑functional agile teams of software/machine learning engineers, data scientists, designers, product managers, and others to build the AI ecosystem within the Group.
* Deliver on time, demonstrating strong commitment to delivering the team mission and accepted backlog.
* Partner with Software Development, Data Science and DevOps to understand their requirements and build well‑architected automated solutions, such as CI/CD pipelines or deployment infrastructure using Google Cloud Platform.
Qualifications
* Bachelor’s degree in Computer Science, Information Technology or related field, or equivalent work experience.
* At least 5 years of proven, hands‑on DevOps engineering experience with major public cloud services, preferring GCP services (Compute Engine, GKE, BigQuery, Cloud Run, Cloud Composer). Google Cloud Professional certifications (Architect, Data Engineer, or DevOps) are a plus.
* Experience developing and mentoring junior engineers, acting as a technical consultant to product managers and stakeholders.
* Ability to apply software development best practices to machine learning projects, including unit testing, DevOps integration, release management, and test‑driven development.
* Proficiency in Kubernetes, Docker, and container orchestration.
* Experience building large‑scale monitoring solutions on Google Cloud Monitoring, Datadog, Prometheus, Grafana, or similar.
* Knowledge of Python and scripting languages (e.g., Bash). Experience with Spark or other big‑data technologies is a plus.
* Strong understanding of Agile and Scrum methodologies and ability to keep the team focused on delivering business value.
* Self‑motivated, strong problem‑solving and learning skills, with flexibility to adapt to changes.
* Fluency in English.
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