Senior ML Platform Engineer

The Role Senior ML Platform Engineer (MLOps)Rakuten Kobo Inc. is seeking avisionary and highly skilled Senior ML Platform Engineertoarchitect, build, and lead the evolution of our internal Machine Learning Platform and MLOps capabilities.In this pivotal role, you will define the strategic roadmap and hands-on implementation for astate-of-the-art, fully automated ML framework on the Google Cloud Platform (GCP).You will be instrumental indesigning and developing the core infrastructure, tools, and services that empower our Data Scientists and ML Engineersto efficiently develop, deploy, monitor, and manage their Machine Learning models throughout their lifecycle. Collaborating closely with Data Scientists, Data Engineers, Platform Engineers, and business stakeholders, you will transform manual ML production processes into a seamless, scalable, and reproducible ML Platform.This groundbreaking position is dedicated tostreamlining the entire ML project lifecycle by providing a robust, self-service platform, ensuring the continuous delivery of significant business value through innovative Machine Learning solutions. Success in this role demands not only profound ML engineering and platform-building expertise but also a strategic, forward-thinking mindset for seamlessly integrating ML/AI into the core of our engineering practices at scale.Experience and Background:8+ years of professional experiencein ML Engineering or related fields, with a significant portion dedicated to ML Platform development.Proven experience leading the design, development, and implementation of a custom ML Platformor significant MLOps infrastructure for an organization. This is themost crucialmust-have.Deep expertise in MLOps tools and their integration into a platform, including:Orchestration:Kubeflow, Airflow, Argo Workflows, Step Functions, Vertex AI Pipelines.Experiment Tracking & Model Registry:MLflow, DVC, Vertex AI ML Metadata, SageMaker Experiments/Model Registry.Model Monitoring & Observability:Prometheus, Grafana, Arize, Sagemaker Model Monitor, Vertex AI Model Monitoring.Data/Model Versioning:DVC, Git-LFS, internal systems.Feature Stores:Feast, Hops-works, or custom-built.CI/CD for ML:Jenkins, GitHub Actions, GitLab CI, BuildKite, ArgoCD (GitOps).Containerization & Orchestration:Docker, Kubernetes, Helm.Strong proficiency in Python.Extensive Cloud Experience, with a strong preference for GCP.This includes hands-on experience with GCP MLOps services (Vertex AI, Dataflow, BigQuery ML, Cloud Build, GKE, Cloud Composer).Experience moving companies from manual to automated processesat scale, particularly in the context of ML development and deployment.Demonstrated Seniority:Ability to lead projects, make architectural decisions, mentor junior engineers, and influence technical strategy. This includes communicating complex technical concepts to non-technical stakeholders.Solid understanding of ML fundamentals(predictive modeling, deep learning, GenAI/LLMs are a plus but secondary to platform expertise).The Skillset:Strong hands-on experience with GCP tools such as:Vertex AI BigQueryCloud Storage Cloud Composer / Airflow Cloud Build and Cloud DeployCloud Functions MLOps framework and Automation:Strong understanding of data ingestion pipelines and experiment tracking tools.Ability to enforce reproducibility and lineage tracking.Familiarity with Kubeflow and/or TFXProven ability to design and implement CI/CD pipeline for ML (automated training, testing, and deployment, integration with GitHub or Cloud Build)Experience with model versioning and registry (Vertex AI Model Registry)Knowledge of Feature Store design.Ability to setup automated monitoring for data and model drift, model performance. Experience setting up observability stacks (logging, metrics, alerts, model health dashboards). Software Engineering and DevOps:Proficiency in Python (mandatory), familiarity with R/Scala/Java as needed.Experience with containerization (docker) and orchestration (Kubernetes, GKE)Strong background in infrastructure-as-a-code (Terraform, Deployment Manager)Ability to implement unit tests, integration tests, and ML-specific validation. Compliance and Best Practices:Knowledge of responsible AI practices (bias, explainability)Familiarity with data governance, security and compliance standards.Strong ability to document and enforce coding standards, review processes, and reproducibility guidelines. Nice to have:Familiarity with the eBook, audiobook, or publishing industry.Contributions to open-source projects related to MLOps or autonomous systems.The Perks: Flexible hours and working environment 4 extended summer long weekends Full benefits starting from your first day Paid Volunteer days, unlimited sick days, and 3% RRSP matching Monthly commuting allowance for hybrid employees Flexible health spending account Training budget + Udemy account Free Kobo device + free weekly e-book or audiobook Weekly Kobo Tech University sessions Maternity/paternity leave top up 90 Day Work from Anywhere program Daily lunch credit when in-office and in-office snacks Dog friendly office