GCP AI/ML Engineer

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.