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Google Cloud Platform AI/ML Engineer

Chicago, IL, US • Posted 30+ days ago • Updated 11 hours ago

Full Time

On-site

Fitment

Dice Job Match Score™

⭐ Evaluating experience...

Job Details

Skills

Evaluation
Lifecycle Management
Data Integration
Data Flow
Cloud Storage
Use Cases
Performance Tuning
Dashboard
Scalability
FOCUS
Orchestration
Management
Collaboration
Extract
Transform
Load
Cloud Computing
IT Management
Mentorship
Regulatory Compliance
Git
Continuous Integration
Continuous Delivery
Testing
Documentation
Workflow
Computer Science
Data Science
Google Cloud
Google Cloud Platform
Vertex
Artificial Intelligence
Training
Storage
Python
Machine Learning Operations (ML Ops)
TensorFlow
PyTorch
scikit-learn
Apache Beam
Docker
Real-time
Machine Learning (ML)
Streaming

Evaluation
Lifecycle Management
Data Integration
Data Flow
Cloud Storage
Use Cases
Performance Tuning
Dashboard
Scalability
FOCUS
Orchestration
Management
Collaboration
Extract
Transform
Load
Cloud Computing
IT Management
Mentorship
Regulatory Compliance
Git
Continuous Integration
Continuous Delivery
Testing
Documentation
Workflow
Computer Science
Data Science
Google Cloud
Google Cloud Platform
Vertex
Artificial Intelligence
Training
Storage
Python
Machine Learning Operations (ML Ops)
TensorFlow
PyTorch
scikit-learn
Apache Beam
Docker
Real-time
Machine Learning (ML)
Streaming

Summary

Job Title: Google Cloud Platform 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 Google Cloud Platform AI/ML Engineer to design, build, and operationalize scalable machine learning solutions on Google Cloud Platform (Google Cloud Platform). 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 Google Cloud Platform-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 (Google Cloud Platform) services:

Vertex AI (Pipelines, Training, Endpoints)
oBigQuery
oCloud 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.

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.

Dice Id: 10441471
Position Id: dc27d4056dbea690ad699a4625a3ce89
Posted 30+ days ago

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