Collaborate closely with business stakeholders and other engineers to deliver impactful solutions.
Integrate services and product features with databases (Cloud SQL, Redis) and messaging queues (Pub/Sub).
Contribute to the development of our MLOps tools for ML models.
Build and optimize large-scale data processing pipelines for feature extraction and data transformation using BigQuery and Dataflow.
Train, evaluate, deploy, and monitor machine learning models on Vertex AI to improve bidding performance.
Stay fast and focused, balancing speed, performance, and scalability to deliver impact.

Requirements

A minimum of 5 years of professional software engineering experience, demonstrating progressive growth and significant hands-on expertise in backend systems development and machine learning engineering
Strong proficiency in Python. Experience with FastAPI is a plus.
Deep understanding and practical experience with Google Cloud Platform (GCP)
Exposure to the end-to-end machine learning lifecycle: data preprocessing, feature engineering, model training, evaluation, deployment, and monitoring in production.
Experience with MLOps principles and practices.
Familiarity with containerization and orchestration (Docker, Kubernetes).
A “get it done” mindset with the ability to make pragmatic decisions balancing speed, performance, technical debt, and scalability.
Strong communication and collaboration skills.

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

PythonFastAPIMLOpsdata preprocessingfeature engineeringmodel trainingmodel evaluationmodel deploymentmodel monitoringbackend systems development

Soft Skills

communicationcollaborationpragmatic decision-makingget it done mindset