Machine Learning Engineer

The Machine Learning Engineer will expand and optimize the company's existing data infrastructure, collaborating with all functional areas of the business and will work across Analytics, Data Engineering and Software Engineering.

The ML Engineer will:

Collaborate with data scientists and analysts to operationalize ML models, streamline workflows, and maintain model reproducibility and versioning.

Design, implement, and manage robust, scalable pipelines for analytics and machine learning, while ensuring seamless data ingestion, transformation, and accessibility.

Maintain and optimize data storage solutions and query performance, prioritizing cost-efficiency and scalability within the Cloud Platform (GCP).

Implement ML Ops best practices, including model CI/CD, feature store management, and metadata tracking.

Automate routine analytics workflows and model lifecycle stages.

Develop monitoring and alerting systems to ensure high reliability of ML pipelines and analytics dashboards.

Drive internal process improvements by refactoring legacy systems and supporting the transition to containerized, serverless frameworks, such as GCP Cloud Run.

Collaborate with engineering and DevOps to refine version control practices, manage infrastructure as code (Terraform), and integrate GitHub Actions for end-to-end automation of analytics, optimization and ML pipelines.

Support CI/CD pipelines for both analytics infrastructure and ML models, enabling continuous delivery of insights and innovation.

Establish data governance, monitoring, and quality protocols to uphold analytical integrity.

Strengthen the use of GCP best practices and data security practices to safeguard sensitive data and ensure regulatory compliance.

Provide technical support for bug resolution, process testing, code reviews and maintain optimal performance across analytics and ML teams.

Qualifications

Bachelor's degree in Computer Science, Engineering, or a related data/analytics field.

5+ years of hands-on experience in data engineering, analytics, and ML infrastructure.

Background in building and optimizing analytics and ML pipelines and tools.

Experience maintaining production-grade infrastructure for machine learning workflows and analytics systems.

Proficiency in Python and SQL (BigQuery experience is preferred).

Experience with Bash scripting is a plus, Linux environments, and cloud services (preferably Google Cloud Platform).

Experience with containerized application environments (e.g., Docker, Kubernetes) and orchestration.

Proficiency with workflow orchestration tools (e.g., Airflow, Luigi).

Strong Git/GitHub version control and DevOps collaboration practices.

Familiarity with infrastructure as code tools (e.g., Terraform).