onsite
AI Engineer - Use Cases & Semantic Layer
AI Engineer
Lead AI initiatives by designing semantic layers and data models across AWS and Azure, driving actionable insights and scalable analytics solutions.
About the role
Key Responsibilities
- Architect and implement semantic layers that translate complex business requirements into reusable data models across AWS Analytics and Azure Cloud environments.
- Collaborate with data scientists and product teams to define AI use cases, ensuring alignment with business objectives and technical feasibility.
- Develop and maintain scalable data pipelines, leveraging cloud services such as Redshift, Snowflake, Synapse, and Azure Data Factory.
- Optimize query performance and data storage strategies to support real‑time analytics and machine learning workloads.
- Document architecture, data models, and best practices for cross‑functional teams.
Requirements
- Proven experience designing semantic layers and data models in cloud analytics platforms.
- Strong proficiency with AWS Analytics services (Athena, Redshift, Glue) and Azure Cloud services (Synapse, Data Factory).
- Hands‑on knowledge of SQL, Python, and data modeling concepts.
- Excellent communication skills and ability to translate technical concepts to non‑technical stakeholders.
- Experience with AI/ML pipelines and model deployment is a plus.