Required Skills & Qualifications
5+ years of experience in Data Engineering, Machine Learning Engineering, or related roles (9 years preferred)
Strong experience with Google Cloud Platform (GCP)
Hands-on expertise with:
Vertex AI
Dataproc
Apache Spark / PySpark
Apache Iceberg
BigQuery
Cloud Storage (GCS)
Strong proficiency in Python and SQL
Experience building distributed data processing and ML pipelines
Understanding of MLOps concepts, model lifecycle management, and deployment strategies
Familiarity with CI/CD tools and Infrastructure as Code

Role Overview
We are seeking a highly skilled Machine Learning Engineer to build, deploy, and manage scalable machine learning solutions on Google Cloud Platform (GCP). The successful candidate will be responsible for operationalizing machine learning models developed by Data Scientists, ensuring reliable execution, monitoring, performance optimization, and integration with enterprise data platforms.

Technical Core & Responsibilities
Machine Learning Platform Engineering
Deploy, execute, and manage machine learning models provided by Data Scientists using Vertex AI
Design and maintain automated ML pipelines for batch and near real-time scoring
Configure and manage Vertex AI training, model registry, endpoints, and prediction services
Monitor model execution, performance, latency, and operational health

Data Engineering & Processing
Develop scalable data processing frameworks using Dataproc, PySpark, and Spark SQL
Build robust data ingestion, transformation, and feature engineering pipelines
Optimize distributed processing workloads for performance and cost efficiency
Ensure data quality, completeness, and consistency across ML workflows

Apache Iceberg Data Management
Design and manage large-scale data lakes using Apache Iceberg
Implement partitioning, schema evolution, versioning, and time-travel capabilities
Optimize Iceberg table performance for machine learning and analytical workloads
Collaborate with data platform teams to establish enterprise data management standards

MLOps & Automation
Implement CI/CD pipelines for ML deployment and model lifecycle management
Automate model retraining, scoring, validation, and monitoring workflows
Build observability frameworks including logging, alerting, metric collection, and operational dashboards
Establish governance controls for model execution and data lineage

Cloud Platform Management
Manage GCP infrastructure supporting machine learning workloads
Optimize compute utilization across Vertex AI, Dataproc, BigQuery, GCS, and related services
Implement security, access controls, and cloud operational best practices
Support production incident resolution and platform reliability initiatives

Preferred Qualifications
Experience with Kubeflow Pipelines or Vertex AI Pipelines
Knowledge of feature stores and model monitoring frameworks
Experience with Docker and Kubernetes
Familiarity with data governance, metadata management, and data lineage tools
Experience in financial services, AML, risk analytics, or large-scale regulated environments

Ideal Candidate Profile
A strong platform-oriented Machine Learning Engineer who can bridge Data Science and Data Engineering teams by transforming analytical models into scalable, governed, and production-ready AI solutions on GCP.

Machine Learning Engineer

Similar Engineering Jobs