ML/AI Engineer- Azure
Tasks
- Create technical documentation
- Define and implement machine learning model lifecycle best practices
- Design deliver and manage genai solutions
- Design deliver and manage industrialized processing pipelines
- Gather and apply knowledge of ml architecture and operations
- Gather technical requirements and estimate planned work
- Implement MLOps and LLMOps frameworks
- Implement llm ml ai automation for scale
- Implement machine learning models into production
- Present solutions and results to clients
Perks/Benefits
- Certificate training programs
- Donation opportunities
- Flexible working hours
- Learning platform access
- Modern office equipment
- Online onboarding
- Remote work option
- Upskilling support
- Wellbeing and health activities
Skills/Tech-stack
Apache Spark | Azure AI | Azure DevOps | Azure Machine Learning | CI/CD | Data Lakehouse | Data Pipelines | Databricks | Deep learning | Docker | GitHub Actions | GitLab | Hive | Kubernetes | LLMOps | Language Models | Large Language Models | MLOps | Machine Learning | Prompt engineering | PySpark | Python | RAG
Education
N/A
Roles
AI | AI Engineer | Engineer | ML Engineer
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