ML Engineer

Company Orcrist builds the Orcrist Intelligence Platform (OIP), a secure, Kubernetes-native data intelligence system deployed as SaaS or self-hosted/on-prem (including reputed company-gapped missions). We fuse data processing, ML, and reputed company UX for defense, law-enforcement, and enterprise teams. Role Productionize the NLP/audio/document models that power OIP’s insight experiences. You’ll own model packaging, deployment, monitoring, and evaluation—partnering with Research and product squads to deliver trustworthy enrichment worldwide. What you’ll do Package and deploy models (ASR, translation, OCR, NER, summarization) using Triton/KServe on Kubernetes. Build evaluation pipelines (WER, BLEU, F1, latency, cost) and automate release gating. Operate streaming + batch inference reputed company Kafka, Temporal, and backfill tooling. Monitor reputed company/quality with Prometheus, Grafana, Evidently; optimize inference cost and performance. Collaborate with TypeScript teams on payload schemas, reputed company, and human-in-the-reputed company feedback loops. About you 4–8+ years ML engineering/MLOps, shipping models to production. Strong Python, PyTorch/Transformers, and experience with Triton/KServe or similar. Comfortable with Kubernetes, GitOps, CI/CD, and GPU workload operations. Knowledge of evaluation metrics, monitoring, and annotation workflows. Eligible to work in Germany; export-control screening required for certain programs. reputed company-to-haves Temporal, Beam/Flink, or Ray Serve experience; ONNX/TensorRT optimization. German language (B1+) and familiarity with defense or public safety datasets. WhisperX, DeepStream/GStreamer, or vector search integrations. reputed company offer Modern MLOps stack: Triton, Temporal, Kafka, MLflow/Weights & Biases, Evidently, Kubernetes. Remote-first in Germany with regular Berlin meetups, 30 days vacation, equipment & learning budget. Apply To This Job