Your newpany
You will be joining a major, forward‑thinking organization that is investing heavily in scalable machine learning platforms and cloud‑native infrastructure. The team is focused on enabling frictionless experimentation, deployment, and production operations for Data Scientists and ML Engineers across the enterprise.
Your new role
As an MLOps Engineer, you will architect and maintain robust, scalable cloud infrastructure using Google Cloud Platform. You will play a key role in automating end‑to‑end ML workflows, optimizing data pipelines, and ensuring the reliability, performance, and usability of the ML platform.
- Architecting, developing, and maintaining scalable cloud infrastructure using
Vertex AI, BigTable, BigQuery, Cloudposer, and Cloud Storage.
Automating and orchestrating ML workflows, integrating data ingestion, feature engineering, model training, and deployment.Enhancing platform usability and scalability to ensure a seamless experience for ML practitioners.Optimizing data pipelines and cloud resources for low‑latency and cost‑efficientperformance.
What you'll need to succeed
5+ years of software engineering experience, focused on cloud infrastructure, data engineering, or ML platforms.Hands‑on experience with GCP services including Vertex AI, BigTable, BigQuery, Cloudposer, and Cloud Storage.Proficiency in Python, Java, or SQL for building scalable backend and data solutions.Experience with Apache Airflow / Cloudposer.Strong knowledge of CI / CD, DevOps tools, and automation frameworks.Familiarity with Docker and Kubernetes.Excellentmunication skills and the ability to thrive in a fast‑paced, collaborative environment.What you'll get in return
The opportunity to influence large‑scale ML platform architecture and core infrastructure.A collaborative, innovation‑focused environment with strong engineering culture.