ML Engineer

🕒 February 25

🇩🇪 Germany – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🤖 Machine Learning Engineer

👻 Ghost score 47%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Orcrist Technologies GmbH

WebsiteLinkedInAll Job Openings

11 - 50 employees

💼 Consulting

🎖️ Defense

📦 Logistics

Consulting • Defense • Logistics

Orcrist Technologies GmbH is a company that builds intelligence-fusion and decision-support solutions for complex environments, combining advanced analytics and AI to help organizations turn uncertainty into strategic advantage. The firm emphasizes enterprise and government-focused offerings, positioning itself as a provider of AI-driven operational intelligence and precision decision tools for public sector and large-organization use cases.

📋 Description

• 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.

🎯 Requirements

• 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.

🏖️ Benefits

• 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.