What are the responsibilities and job description for the AI Engineer Google Cloud Platform (Google Cloud Platform) position at Jobs via Dice?
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We are seeking a highly skilled AI Engineer with strong experience in cloud-based AI/ML solutions, preferably on Google Cloud Platform (Google Cloud Platform). The ideal candidate will design, build, deploy, and scale machine learning and AI systems in a production cloud environment.
Key Responsibilities
Please ensure resumes clearly highlight:
We are seeking a highly skilled AI Engineer with strong experience in cloud-based AI/ML solutions, preferably on Google Cloud Platform (Google Cloud Platform). The ideal candidate will design, build, deploy, and scale machine learning and AI systems in a production cloud environment.
Key Responsibilities
- Design, develop, and deploy AI/ML models in cloud environments (Google Cloud Platform preferred; AWS/Azure acceptable)
- Build end-to-end ML pipelines including data ingestion, feature engineering, training, evaluation, and deployment
- Implement MLOps practices using CI/CD, monitoring, versioning, and model lifecycle management
- Work with large datasets using cloud-native data services
- Optimize model performance, scalability, and cost in cloud environments
- Collaborate with data engineers, product teams, and stakeholders to translate business needs into AI solutions
- Ensure security, compliance, and best practices in cloud AI implementations
- Strong experience as an AI Engineer / ML Engineer
- Hands-on experience with Google Cloud Platform (Vertex AI, BigQuery, Cloud Storage, Dataflow, Pub/Sub)
- Proficiency in Python and ML libraries (TensorFlow, PyTorch, Scikit-learn)
- Experience with REST APIs, microservices, and containerization (Docker, Kubernetes)
- Solid understanding of ML algorithms, deep learning, and model evaluation
- Experience with SQL and data processing frameworks
- Experience with AWS or Azure in addition to Google Cloud Platform
- Knowledge of LLMs, GenAI, NLP, or Computer Vision use cases
- Experience with Terraform / Infrastructure as Code
- Exposure to real-time or streaming ML systems
- Pure Data Scientists with no production deployment experience
- Candidates without cloud AI/ML implementation experience
Please ensure resumes clearly highlight:
- Google Cloud Platform AI/ML project experience
- Model deployment and MLOps responsibilities
- End-to-end ownership of AI solutions