Orcrist Technologies
Technology
MLEngineer
Neural analysis suggests this role is
optimal for Senior candidates.
“ML Engineer at Orcrist Technologies. Skills: ML Engineering, MLOps, Kubernetes, PyTorch. Build ML prototype vertical slices. Connect ingest/processing to inference”
What You'll Achieve.
Build adoption-ready prototype vertical slices; Hand off clear artifacts; Productize and own long-term
Industry & Context.
Troubleshoot production-like clusters
EU/NATO citizenship preferred, Export-control screening
What They're Looking For.
Must Have
3+ years ML engineering/MLOps experience, Python and hands-on PyTorch/Transformers, Practical Kubernetes + containers, Evaluation discipline and monitoring
Nice to Have
GPU serving/optimization experience, Streaming/pipeline tooling, Search/vector/graph integrations, German language (B1+), Experience with regulated/public-sector datasets
What You'll Do.
Build ML prototype vertical slices
Connect ingest/processing to inference
Create evaluation harnesses
Create decision artifacts
Package prototypes for adoption
Containerize services
Define reproducible deployments
Produce runbooks/checklists
Partner on dataset curation
Partner on annotation loops
Partner on experiment tracking
Partner on safe iteration
Make prototypes operationally credible
Instrument prototypes
How You'll Work.
Team & Collaboration
Partner with Research; Partner with Data Engineering
Communication Scope
Communicate tradeoffs clearly
Full Job Description
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, Ra
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