AI Engineer (GCP)
Tasks
- Architect oversee data pipelines workflows
- Coordinate data engineering and software engineering teams
- Deploy and oversee AI applications
- Design AI solution architectures
- Lead development implement validate machine learning solutions
- Lead mentor cross functional teams
- Present findings recommendations to stakeholders
- Prioritize control tasks in Agile Scrum
- Research test select tools technologies
- Translate business problems into data science problems
Perks/Benefits
- Career growth
- Conference support
- Flexible work hours
- Integration budget
- Medical insurance
- Paid time off
- Professional training
- Psychotherapy services
- Remote work
- Team building events
- Wellbeing support
- Work Equipment Supplied
Skills/Tech-stack
Agile | CI/CD | Computer Vision | Databricks | Docker | Embeddings | GCP | GCP services | Generative AI | GitHub Actions | Hadoop | Kafka | Kubernetes | Language Models | Language Processing | Large Language Models | MLOps | Machine Learning | MongoDB | NLP | Natural Language | Natural Language Processing | Predictive Modeling | Prompt engineering | PyTorch | Python | RAG | Retrieval-Augmented Generation | SQL | Scikit-learn | Scrum | Semantic Search | Snowflake | Spark | TensorFlow
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Roles
AI | AI Engineer | Engineer | Learning Engineer | Machine Learning Engineer
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