Senior AI Platform Engineer
New
J
JobgetherAI Platform Engineering
Based in GermanyFull-TimeSenior
Salary not disclosed
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Job Details
- Languages
- English
- Experience
- 5+ years
- Required Skills
- PythonSQLSnowflakeAirflowdbtDatabricksGenerative AI
Requirements
- 5+ years of professional experience in data engineering, platform engineering, backend engineering, or a closely related discipline.
- Strong proficiency in Python and SQL.
- Hands-on experience building and deploying production applications or services using LLMs and generative AI.
- Practical experience with RAG, embeddings, vector search, tool/function calling, AI agents, or enterprise knowledge systems.
- Strong data engineering fundamentals, including data pipelines, data modeling, data quality, and secure data access.
- Experience with Snowflake, Databricks, or comparable modern data platforms, together with tools such as dbt, Airflow, or similar technologies.
- Proven experience building shared AI infrastructure, platforms, or reusable AI capabilities.
- Experience taking AI systems from experimentation and proof of concept through reliable production deployment.
- Solid understanding of security, authentication and authorization, privacy, access control, and data governance.
- Fluent professional proficiency in English, both written and spoken.
- Reliable home internet connection suitable for fully remote work.
Responsibilities
- Design and build reusable platform capabilities supporting LLM applications, AI agents, RAG, tool calling, and AI workflows.
- Develop scalable data and knowledge pipelines covering ingestion, embeddings, retrieval, vector search, metadata, and knowledge management.
- Build secure integrations between AI applications, enterprise data, and business systems using APIs, MCP, tool calling, and comparable integration patterns.
- Develop reusable frameworks, libraries, services, SDKs, and developer tooling that allow engineering teams to build AI applications efficiently.
- Establish technical standards and patterns for AI deployment, observability, evaluation, monitoring, and lifecycle management.
- Define and improve approaches for measuring AI quality, accuracy, latency, reliability, security, and cost.
- Take AI capabilities from experimentation and prototyping through reliable, scalable, and maintainable production deployment.
- Ensure AI platform capabilities comply with security, privacy, authentication, authorization, access control, and data governance requirements.
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