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 air-gapped missions). We fuse data processing, ML, and intuitive 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 via Kafka, Temporal, and backfill tooling. - Monitor drift/quality with Prometheus, Grafana, Evidently; optimize inference cost and performance. - Collaborate with TypeScript teams on payload schemas, contracts, and human-in-the-loop 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. Nice-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. What we 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. Originally posted on Himalayas