About the role

We build and operate games at scale — millions of players, billions of telemetry events, and a back office that has to keep pace with all of it. That means our data estate spans three worlds that rarely sit under one roof: the AI/KI layer that powers personalisation, matchmaking economics and fraud detection; the BI layer that turns raw player and revenue telemetry into decisions; and the SAP R/3 core that runs finance, procurement and controlling for the business behind the games.

We're hiring an Architect Engineer to own the design across all three — not to sit in a diagramming tool, but to design, prototype and stay close enough to the code to be credible with the teams shipping it.

This is a rare mandate. We are explicitly looking for someone who has genuinely worked across the AI/BI/ERP boundary, not someone robust in one and adjacent to the others.

What you'll do

- Own the end-to-end target architecture connecting SAP R/3 (FI/CO, MM, SD) to the analytics and AI platforms — data contracts, extraction patterns, latency budgets, reconciliation.
- Design and review the AI/KI architecture: feature stores, model serving, retraining pipelines, evaluation and monitoring for models running in a live-service gaming environment.
- Define the BI layer — semantic models, dimensional design, governed metrics — so finance, live-ops, monetisation and studio leadership work from one version of the truth.
- Build reference implementations and PoCs. You write code; you don't hand a deck to engineering and walk away.
- Set standards for data quality, lineage, master data and access control across player data and financial data (GDPR relevant throughout).
- Drive the modernisation path off R/3 — evaluate and sequence the move toward S/4HANA and cloud-native analytics without breaking month-end close.
- Act as the translation layer between studio/live-ops teams, the SAP functional side, and the data/ML org.
- Review designs, mentor senior engineers, and make the trade-off calls that don't have a clean answer.





Must-have (all three — non-negotiable)

1. KI / AI

- Production ML or AI systems you personally architected — not pilots, not notebooks.
- Depth in at least one of: recommendation/personalisation, anomaly & fraud detection, forecasting, or LLM-based systems.
- MLOps fluency: pipeline orchestration, model versioning, drift monitoring, serving infrastructure.
- Python plus a modern ML stack (PyTorch / TensorFlow / scikit-learn) and at least one cloud ML platform (AWS SageMaker, Azure ML, or GCP Vertex).

2. Business Intelligence

- Enterprise-grade BI architecture ownership: warehouse/lakehouse design, dimensional modelling (Kimball or Data Vault), semantic layer governance.
- Strong SQL and hands-on with a modern warehouse — Snowflake, BigQuery, Databricks or Redshift.
- Delivered on at least one major BI platform: Power BI, Tableau, Looker, or SAP Analytics Cloud.
- ELT/ETL tooling depth (dbt, Airflow, Talend, or equivalent).

3. SAP R/3

- Real, hands-on SAP R/3 / ECC experience — you know how the system is actually put together, not just that it exists.
- Working knowledge of core modules: FI/CO essential; MM and SD strongly preferred.
- Data extraction and integration from R/3 in practice: SAP BW, ODP/Operational Data Provisioning, SLT, CDS views, BAPIs/RFCs, or IDocs.
- Understanding of the R/3 data model well enough to design reliable downstream pipelines and reconcile against the source of truth.

Also required

- Track record designing systems that survived real scale and real incidents.
- Written and spoken English at working proficiency. German is a strong plus.
- Comfortable operating fully remote and asynchronously, with disciplined written communication.

Nice to have

- Gaming, live-service, or another high-telemetry consumer domain (adtech, streaming, marketplaces).
- S/4HANA migration experience, or having planned one.
- SAP BW/4HANA or SAP Datasphere.
- Event streaming at scale — Kafka, Kinesis, Pub/Sub.
- Player analytics: LTV modelling, churn, in-game economy design, cohort and monetisation analysis.
- Infrastructure-as-code and Kubernetes.
- SAP or cloud architecture certifications.

📌 Architect Engineer (India)
🏢 Versatile.club
📍 India