Senior Software Engineer - Data & AI Platform (m/w/d)
Confirmed on the employer's own hiring board on Nov 10, 2026. First seen by Alion on Nov 10, 2026. Vivenu scores A on the Alion truth index.
With over 10 million end users and usage quadrupling annually, our infrastructure now handles over 1 billion requests per month - and counting. Our API-first platform solves complex system challenges at scale - delivering performance, flexibility, and reliability for the world’s leading live entertainment brands.
As a Senior Software Engineer - Data & AI Platform (d/f/m) your responsibilities will include:
Build Analytical Query Engines: Connect our transactional backends (MongoDB) with modern analytical event stores (ClickHouse) to support sub-second aggregations across millions of ticket transactions.
Implement AI Retrieval & Reporting Workflows: Build and deploy Retrieval-Augmented Generation (RAG) pipelines, semantic parsers, and agentic query supervisors that convert natural language prompts into deterministic, accurate SQL and aggregation queries.
Maintain Low-Latency Reporting APIs: Write high-performance production services in Goor TypeScript/Node.jsthat power real-time reporting dashboards for global organizers without degrading transactional processing.
Centralize Data Flows & Auditing: Partner with platform and security teams to build clean data schemas, strict audit trails, and automated evaluation suites that catch hallucinated data or metric drifts before they reach clients.
Sprint Scoping & Architecture: Collaborate directly in monthly in-person architecture sessions in Frankfurt alongside the VP of Engineering, CTO, and engineering leads to define technical milestones.
Your Responsibilities:
What you will need to succeed in this role:
5+ years of hands-on experience building high-throughput, distributed production services using Go, Python, or TypeScript/Node.js.
Analytical Warehousing Experience: Direct, hands-on production experience with ClickHouse(or closely related low-latency analytical engines like DuckDB, Snowflake, or Apache Pinot) for high-volume event streams.
Database Mastery: Solid practical experience with both document/NoSQL stores (MongoDB) and relational engines (PostgreSQL).
Production AI / RAG Engineering: Proven experience deploying production RAG systems, function calling, evaluation frameworks, and supervisor architectures (e.g., LangGraph, LlamaIndex, or custom orchestration layers).
Event-Driven Architectures: Strong understanding of distributed message streaming using Kafka, RabbitMQ, or cloud pub/sub mechanisms.
Pragmatic Mindset: High autonomy and scrappiness-you choose reliable, deterministic validation layers and battle-tested databases over fragile AI hype.