ML Engineer

Responsibilities

• 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 and batch inference via Kafka, Temporal, and backfill tooling
• Monitor drift and quality with Prometheus, Grafana, and Evidently
• Optimize inference cost and performance
• Collaborate with TypeScript teams on payload schemas, contracts, and human-in-the-loop feedback loops

Requirements

• 4–8+ years of ML engineering or MLOps experience shipping models to production
• Strong Python and PyTorch/Transformers skills
• Experience with Triton/KServe or similar
• Proficiency with Kubernetes, GitOps, CI/CD, and GPU workload operations
• Knowledge of evaluation metrics, monitoring, and annotation workflows
• Eligibility to work in Germany

Preferred Qualifications

• Experience with Temporal, Beam/Flink, or Ray Serve
• ONNX/TensorRT optimization experience
• German language proficiency (B1+)
• Familiarity with defense or public safety datasets
• Experience with WhisperX, DeepStream/GStreamer, or vector search integrations

About the Company

Orcrist builds the Orcrist Intelligence Platform (OIP), a secure, Kubernetes-native data intelligence system deployed as SaaS or self-hosted/on-prem. We fuse data processing, ML, and intuitive UX for defense, law-enforcement, and enterprise teams.