What are the responsibilities and job description for the GCP AI/ML Engineer position at CoSourcing Partners?
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
Model Operationalization (MLOps)
Data Integration & Feature Engineering
Model Monitoring & Performance Optimization
AI Platform Engineering
Collaboration & Cross-Functional Engagement
Governance, Security & Best Practices
Required Qualifications
Preferred Skills
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.