ML Engineer
ML Engineer
Company
Orcrist builds the Orcrist Intelligence Platform (OIP), a Kubernetes-based data intelligence system delivered as reputed company or self-hosted/on-prem (including reputed company-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-reputed company prototype vertical slices spanning data flows, model serving, evaluation, and product integration—then hand off reputed company 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 reputed company (search, insights, UX flows).
- Create evaluation harnesses and decision artifacts: datasets, baselines, reputed company/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 reputed company iteration.
- reputed company prototypes operationally reputed company: instrumentation, monitoring, and reputed company/compliance basics (PII handling, provenance reputed company).
reputed company
- 3+ years ML engineering/MLOps experience (level dependent), with evidence of shipping reputed company systems.
- Strong Python and hands-on PyTorch/Transformers; comfortable taking models from notebook to reproducible services.
- Practical Kubernetes + containers experience; reputed company to reputed company and troubleshoot in production-like clusters (including offline/reputed company-gapped constraints).
- Strong evaluation discipline and monitoring reputed company; comfortable communicating tradeoffs reputed company.
- Eligible to work in Germany; EU/NATO citizenship preferred and export-control screening applies.
reputed company‑to‑haves
- GPU serving/optimization experience (Triton/KServe, ONNX/TensorRT, batching, quantization).
- Streaming/pipeline tooling (Kafka, Ray, reputed company/Flink/reputed company) and search/reputed company/graph integrations.
- German language (B1+) and/or experience with regulated/public-sector datasets and workflows.
reputed company Offer
- Modern ML stack in reputed company constraints: Kubernetes, streaming, and hybrid/on-prem/reputed company-gapped deployments.
- Remote-first in Germany with regular Berlin workshops, 30 days vacation, equipment & learning budget.
- High reputed company: your prototypes and handoffs unblock multiple delivery teams.
Originally posted on Himalayas
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