Orcrist Technologies
MLEngineer
Neural analysis suggests this role is
optimal for Senior candidates.
“ML Engineer at Orcrist Technologies. Skills: MLOps, Model productionization, Kubernetes, Inference optimization. Package models. Deploy models”
Industry & Context.
Troubleshooting
Eligible for export-control screening
What They're Looking For.
Must Have
4–8+ years ML engineering/MLOps, Python, PyTorch/Transformers, Triton/KServe or similar, Kubernetes, GitOps, CI/CD, GPU workload operations, Evaluation metrics knowledge, Monitoring knowledge, Annotation workflows knowledge, Eligible for export-control screening
Nice to Have
Temporal experience, Beam/Flink experience, Ray Serve experience, ONNX optimization, TensorRT optimization, German language (B1+), Familiarity with defense datasets, Familiarity with public safety datasets, WhisperX experience, DeepStream experience, GStreamer experience, Vector search integrations
What You'll Do.
Build evaluation pipelines
Automate release gating
Operate streaming inference
Operate batch inference
Optimize inference cost
Optimize inference performance
Collaborate on payload schemas
Collaborate on contracts
Collaborate on feedback loops
How You'll Work.
Team & Collaboration
Research squads; Product squads; TypeScript teams
Full Job Description
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.
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