Join Aibidia's Research and Development team as a Machine Learning/Ops Engineer. You'll create and expand our range of SaaS solutions, playing a pivotal role in delivering an exceptional product to clients such as Nokia, Dyson, Bridgestone, and Olympus. Your work will help revolutionize cross-border business management.

What You'll Do

Design, build, and maintain end-to-end machine learning pipelines from data ingestion to deployment and performance monitoring.
Automate ML workflows including model training, testing, and deployment using CI/CD best practices.
Manage and optimize infrastructure for model serving using containerization (Docker), orchestration (Kubernetes), and Infrastructure-as-Code tools (e.g., Terraform) across cloud environments.
Ensure reproducibility, versioning, and traceability of data, features, and models across the ML lifecycle.
Implement monitoring systems for model performance, including accuracy, latency, data drift, and degradation detection.
Collaborate with data scientists to transition research prototypes into scalable production-ready services.
Lead model lifecycle management practices including retraining, rollback strategies, A/B testing, and shadow deployments.
Ensure compliance with data privacy, security, and governance standards, including responsible AI principles.

What We're Looking For

ML pipeline orchestration using tools like Airflow, Kubeflow, or Prefect.
Experience setting up automated testing and deployment (e.g., GitHub Actions, Jenkins, GitLab CI, Azure DevOps Pipelines).
Proficient with Docker, Kubernetes, Terraform for scalable deployments.
Experience with MLflow, TensorFlow Serving, TorchServe, or similar.
Experience setting up observability tools to track model/data drift, latency, and failures.
Experience with AWS/GCP/Azure, especially their ML tools (e.g., SageMaker, Vertex AI).

Nice to Have

Knowledge of Kafka, Spark Streaming, or Flink for streaming data processing.
Experience using Terraform for provisioning.
Experience managing and deploying large language models or embedding pipelines.
Experience managing compute/storage costs in cloud-based ML workflows.

Technical Stack

Docker, Kubernetes, Terraform, Airflow, Kubeflow, Prefect
GitHub Actions, Jenkins, GitLab CI, Azure DevOps Pipelines
MLflow, TensorFlow Serving, TorchServe
AWS, GCP, Azure, SageMaker, Vertex AI
Kafka, Spark Streaming, Flink

Team & Environment

You will be a key member of the Research and Development team, collaborating to build and scale Aibidia's product suite.

Benefits & Compensation

A fair share of Aibidia's success, including a competitive compensation and incentive package.
Flexible working hours with a hybrid working policy.
Comprehensive healthcare package.
Genuine drive towards physical and mental wellbeing, with initiatives by an internal organisational health and wellbeing committee.
Regular team social events including Aibidia's summer and winter parties.
The latest technology to ensure you can do your best work.
Performance-based growth as part of the company culture and a designated learning budget for every employee.
An opportunity to be part of a global, fast-growing SaaS company.
A non-hierarchical atmosphere and stellar culture at the office.