Senior AI Platform Engineer
Tasks
- Build secure integrations between AI applications and enterprise data
- Create reusable frameworks libraries services SDKs and developer tooling
- Design and build reusable platform capabilities for LLM applications
- Develop scalable data and knowledge pipelines
- Ensure compliance with security privacy authentication authorization and data governance
- Establish standards for AI deployment observability evaluation monitoring and lifecycle management
- Evaluate AI models frameworks and infrastructure for business value
- Improve platform architecture and engineering standards for reusable AI capabilities
- Measure AI quality accuracy latency reliability security and cost
- Move AI systems from experimentation to production deployment
- Promote data engineering and platform practices for data modeling quality and secure access
- Translate business needs into practical technical solutions
Perks/Benefits
- Asynchronous collaboration
- Fully remote work
- International distributed team
- Mental health support
- Paid time off
- Parental leave
- Technical ownership and growth
- Wellbeing allowance
- Work from home
- Work from home stipend
Skills/Tech-stack
AI Agents | API | AWS | AWS Bedrock | Access Control | Airflow | Authentication | Authorization | DBT | Data Governance | Data Modeling | Data Pipelines | Data Quality | Databricks | Embeddings | Evaluation | Generative AI | LLM | Langchain | Langgraph | MCP | Model Context Protocol | Monitoring | Observability | Privacy | Python | RAG | SDK | SDKs | SQL | Security | Snowflake | Tool-Calling | Vector Databases | Vector Search
Education
N/A
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