Job description

Job Title: GCP AI/ML Engineer Duration: 6 months Contract to hire Location: Chicago is the preferred location, but open to candidates from anywhere in the U.S. Role Overview We are seeking a talented and experienced GCP AI/ML Engineer to design, build, and operationalize scalable machine learning solutions on Google Cloud Platform (GCP). This role focuses on developing production-grade ML pipelines, automating workflows, and ensuring reliability and governance across enterprise AI platforms. The ideal candidate will have strong expertise in Vertex AI, MLOps, and cloud-native ML architectures, with a passion for turning data science models into scalable, production-ready systems. Key Responsibilities ML Pipeline Development & Automation • Build, deploy, and manage production-grade machine learning pipelines using Vertex AI Pipelines and GCP-native services. • Design automated workflows for data ingestion, feature engineering, model training, evaluation, and inference. • Orchestrate ML workflows using Python, Vertex AI, BigQuery, and Cloud Storage. • Ensure pipelines are modular, reusable, and scalable across use cases. Model Operationalization (MLOps) • Operationalize the end-to-end ML lifecycle, including: • Model training • Deployment • Monitoring • Retraining and lifecycle management • Deploy models using Vertex AI endpoints with support for online and batch predictions. • Implement robust CI/CD pipelines for ML artifacts and workflows. • Enable automated model retraining and versioning strategies. Data Integration & Feature Engineering • Enable seamless data flows across data lakes, warehouses, and ML platforms. • Design and manage feature pipelines for training and inference datasets. • Integrate with BigQuery, Cloud Storage, and streaming sources to support real-time and batch ML use cases. • Ensure consistency between training and serving data pipelines. Model Monitoring & Performance Optimization • Implement model monitoring solutions to track: • Prediction accuracy • Data drift and concept drift • Model performance degradation • Set up alerting mechanisms and dashboards for proactive issue detection. • Optimize model performance and infrastructure for scalability, latency, and cost efficiency. AI Platform Engineering • Build and enhance enterprise AI/ML platforms with a focus on: • Automation • Observability • Reliability • Develop standardized frameworks for repeatable and governed ML deployments. • Establish best practices for MLOps, pipeline orchestration, and infrastructure management. Collaboration & Cross-Functional Engagement • Collaborate closely with: • Data Scientists to productionize models • Data Engineers for data pipeline integration • Architects for scalable cloud designs • Translate business requirements into deployable ML solutions. • Provide technical leadership and mentoring on ML engineering practices. Governance, Security & Best Practices • Implement model governance frameworks including auditability, lineage, and compliance. • Ensure secure handling of data and models using IAM roles and access policies. • Promote best practices in: • Code versioning (Git) • CI/CD • Testing and validation • Drive documentation and standardization across ML workflows. Required Qualifications • Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or related field. • 4+ years of experience in machine learning engineering or MLOps. • Hands-on experience with Google Cloud Platform (GCP) services: • Vertex AI (Pipelines, Training, Endpoints) o BigQuery o Cloud Storage • Strong programming skills in Python. • Experience building and deploying end-to-end ML pipelines. • Strong understanding of ML lifecycle and MLOps principles. Preferred Skills • Experience with TensorFlow, PyTorch, or Scikit-learn. • Familiarity with Kubeflow Pipelines or Apache Beam. • Experience with Docker and containerized deployments. • Knowledge of real-time ML inference and streaming architectures. • Hands-on experience with model monitoring tools and frameworks. • Understanding of feature stores and feature engineering pipelines.

Requirements

Must-haves

Job Title: GCP AI/ML Engineer
Duration: 6 months Contract to hire
The ideal candidate will have strong expertise in Vertex AI, MLOps, and cloud-native ML architectures, with a passion for turning data science models into scalable, production-ready systems.
ML Pipeline Development & Automation
Build, deploy, and manage production-grade machine learning pipelines using Vertex AI Pipelines and GCP-native services.
Design automated workflows for data ingestion, feature engineering, model training, evaluation, and inference.
Orchestrate ML workflows using Python, Vertex AI, BigQuery, and Cloud Storage.
Ensure pipelines are modular, reusable, and scalable across use cases.
Model Operationalization (MLOps)
Operationalize the end-to-end ML lifecycle, including:
Model training
Retraining and lifecycle management
Deploy models using Vertex AI endpoints with support for online and batch predictions.
Implement robust CI/CD pipelines for ML artifacts and workflows.
Enable automated model retraining and versioning strategies.
Data Integration & Feature Engineering
Enable seamless data flows across data lakes, warehouses, and ML platforms.

Nice-to-haves

Location: Chicago is the preferred location, but open to candidates from anywhere in the U.S.
Experience with TensorFlow, PyTorch, or Scikit-learn.
Familiarity with Kubeflow Pipelines or Apache Beam.
Experience with Docker and containerized deployments.
Knowledge of real-time ML inference and streaming architectures.
Hands-on experience with model monitoring tools and frameworks.
Understanding of feature stores and feature engineering pipelines.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

gcpaimlmachine learningpythonci/cddocker

Platform: catsone
Effort: medium