About the Job:
At the company, we’re driven by a relentless pursuit of innovation—pushing boundaries in engineering, AI, and robotics. Our new Data Intelligence team sits at the heart of this mission: shaping the company’s future through data. Here, we blend creativity, precision, and audacity to power intelligent products. We craft data strategies and pipelines that fuel the next generation of connected devices.
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In this role, you will design, build, and maintain the backbone of our Machine Learning lifecycle. You will be responsible for the "industrialization" of AI, moving models from experimental notebooks into robust, production-grade pipelines. Your mission is to automate the journey from raw data curation to model deployment, ensuring our CI/CD cycles are fast, observable, and reproducible.
Key Responsibilities:
- End-to-End Pipeline Orchestration: Build and manage automated workflows for data preparation, feature engineering, model training, and evaluation.
- Machine Learning CI/CD/CT Implementation: Develop Continuous Integration, Continuous Deployment, and Continuous Training systems.
- Infrastructure as Code (IaC): Manage scalable Machine Learning infrastructure using tools like MLFlow.
- Model Monitoring & Observability: Implement dashboards and alerts for model drift, data skew, and system performance (latency/throughput).
- Registry Management: Maintain the Model Registry and Feature Store to ensure versioning and lineage across all experiments.
- Security & Compliance: Ensure data privacy and secure access controls throughout the ML lifecycle.
About You:
- 3+ years in DevOps, Data Engineering, or MLOps roles.
- Proven track record of taking at least one ML project from a research phase to a high-availability production environment.
- Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related technical field.
- Expertise in orchestration tools such as Kubeflow, Airflow, Dagster, or Prefect.
- Mastery of Docker and Kubernetes (K8s) for managing distributed training and inference.
- Deep experience with cloud platforms like AWS (SageMaker), GCP (Vertex AI), or Azure ML.
- Advanced Git workflows and experience with DVC (Data Version Control) or MLflow.
- Experience with CI/CD frameworks such as GitHub Actions, GitLab CI, or Jenkins specifically for ML artifacts.
- High proficiency in Python and Bash for automation.
Language Requirements:
Not specified; however, proficiency in English may be assumed based on the job description context.
The company is an equal opportunity employer, welcoming applications from all backgrounds and ensuring that employment decisions are made without regard to race, colour, religion, national or ethnic origin, sex, sexual orientation, gender identity or expression, age, disability, protected veteran status, or any other dimension of diversity.
