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AI Summary
Incubate and validate new ML initiatives by building end-to-end prototype vertical slices from data ingestion to product integration. Create evaluation harnesses and package prototypes for delivery teams to productize.
ML Engineer
Company
Orcrist builds the Orcrist Intelligence Platform (OIP), a Kubernetes-based data intelligence system delivered as SaaS or self-hosted/on-prem (including air-gapped deployments). We combine data processing, ML/AI, and a modern web application to support mission-critical customers across public and private sectors.
Role
Incubate and validate new ML initiatives end-to-end. On Innovation, you’ll build adoption-ready prototype vertical slices spanning data flows, model serving, evaluation, and product integration—then hand off clear artifacts so delivery teams can productize and own them long-term.
What you'll do
- Build ML prototype vertical slices that connect ingest/processing to inference and visible product outcomes (search, insights, UX flows).
- Create evaluation harnesses and decision artifacts: datasets, baselines, quality/latency/cost metrics, and go/no-go recommendations.
- Package prototypes for adoption: containerize services, define reproducible deployments, and produce runbooks/checklists.
- Partner with Research and Data Engineering on dataset curation, annotation loops, experiment tracking, and safe iteration.
- Make prototypes operationally credible: instrumentation, monitoring, and security/compliance basics (PII handling, provenance mindset).
About You
- 3+ years ML engineering/MLOps experience (level dependent), with evidence of shipping real systems.
- Strong Python and hands-on PyTorch/Transformers; comfortable taking models from notebook to reproducible services.
- Practical Kubernetes + containers experience; able to deploy and troubleshoot in production-like clusters (including offline/air-gapped constraints).
- Strong evaluation discipline and monitoring mindset; comfortable communicating tradeoffs clearly.
- Eligible to work in Germany; EU/NATO citizenship preferred and export-control screening applies.
Nice‑to‑haves
- GPU serving/optimization experience (Triton/KServe, ONNX/TensorRT, batching, quantization).
- Streaming/pipeline tooling (Kafka, Ray, Beam/Flink/Spark) and search/vector/graph integrations.
- German language (B1+) and/or experience with regulated/public-sector datasets and workflows.
What We Offer
- Modern ML stack in real constraints: Kubernetes, streaming, and hybrid/on-prem/air-gapped deployments.
- Remote-first in Germany with regular Berlin workshops, 30 days vacation, equipment & learning budget.
- High leverage: your prototypes and handoffs unblock multiple delivery teams.
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