ML Engineer
ML Engineer
Company
Orcrist builds the Orcrist Intelligence Platform (OIP), a Kubernetes-based data intelligence system delivered as SaaS or self-hosted/on-prem (including air-gapped deployments). We combine data processing, ML/AI, and a modern web application to support mission-critical customers across public and private sectors.
Role
Incubate and validate new ML initiatives end-to-end. On Innovation, you’ll build adoption-ready prototype vertical slices spanning data flows, model serving, evaluation, and product integration—then hand off clear artifacts so delivery teams can productize and own them long-term.
What you'll do
Build ML prototype vertical slices that connect ingest/processing to inference and visible product outcomes (search, insights, UX flows).
Create evaluation harnesses and decision artifacts: datasets, baselines, quality/latency/cost metrics, and go/no-go recommendations.
Package prototypes for adoption: containerize services, define reproducible deployments, and produce runbooks/checklists.
Partner with Research and Data Engineering on dataset curation, annotation loops, experiment tracking, and safe iteration.
Make prototypes operationally credible: instrumentation, monitoring, and security/compliance basics (PII handling, provenance mindset).
About You
3+ years ML engineering/MLOps experience (level dependent), with evidence of shipping real systems.
Strong Python and hands-on PyTorch/Transformers; comfortable taking models from notebook to reproducible services.
Practical Kubernetes + containers experience; able to deploy and troubleshoot in production-like clusters (including offline/air-gapped constraints).
Strong evaluation discipline and monitoring mindset; comfortable communicating tradeoffs clearly.
Eligible to work in Germany; EU/NATO citizenship preferred and export-control screening applies.
Nice‑to‑haves
GPU serving/optimization experience (Triton/KServe, ONNX/TensorRT, batching, quantization).
Streaming/pipeline tooling (Kafka, Ray, Beam/Flink/Spark) and search/vector/graph integrations.
German language (B1+) and/or experience with regulated/public-sector datasets and workflows.
What We Offer
Modern ML stack in real constraints: Kubernetes, streaming, and hybrid/on-prem/air-gapped deployments.
Remote-first in Germany with regular Berlin workshops, 30 days vacation, equipment & learning budget.
High leverage: your prototypes and handoffs unblock multiple delivery teams.
Company
Orcrist builds the Orcrist Intelligence Platform (OIP), a Kubernetes-based data intelligence system delivered as SaaS or self-hosted/on-prem (including air-gapped deployments). We combine data processing, ML/AI, and a modern web application to support mission-critical customers across public and private sectors.
Role
Incubate and validate new ML initiatives end-to-end. On Innovation, you’ll build adoption-ready prototype vertical slices spanning data flows, model serving, evaluation, and product integration—then hand off clear artifacts so delivery teams can productize and own them long-term.
What you'll do
Build ML prototype vertical slices that connect ingest/processing to inference and visible product outcomes (search, insights, UX flows).
Create evaluation harnesses and decision artifacts: datasets, baselines, quality/latency/cost metrics, and go/no-go recommendations.
Package prototypes for adoption: containerize services, define reproducible deployments, and produce runbooks/checklists.
Partner with Research and Data Engineering on dataset curation, annotation loops, experiment tracking, and safe iteration.
Make prototypes operationally credible: instrumentation, monitoring, and security/compliance basics (PII handling, provenance mindset).
About You
3+ years ML engineering/MLOps experience (level dependent), with evidence of shipping real systems.
Strong Python and hands-on PyTorch/Transformers; comfortable taking models from notebook to reproducible services.
Practical Kubernetes + containers experience; able to deploy and troubleshoot in production-like clusters (including offline/air-gapped constraints).
Strong evaluation discipline and monitoring mindset; comfortable communicating tradeoffs clearly.
Eligible to work in Germany; EU/NATO citizenship preferred and export-control screening applies.
Nice‑to‑haves
GPU serving/optimization experience (Triton/KServe, ONNX/TensorRT, batching, quantization).
Streaming/pipeline tooling (Kafka, Ray, Beam/Flink/Spark) and search/vector/graph integrations.
German language (B1+) and/or experience with regulated/public-sector datasets and workflows.
What We Offer
Modern ML stack in real constraints: Kubernetes, streaming, and hybrid/on-prem/air-gapped deployments.
Remote-first in Germany with regular Berlin workshops, 30 days vacation, equipment & learning budget.
High leverage: your prototypes and handoffs unblock multiple delivery teams.