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Machine Learning Engineer

Orcrist TechnologiesRemote / Berlin
Remote Full-time

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Experience Level

Mid to Senior

Qualifications

Qualifications 3+ years of ML engineering or MLOps experience. Strong proficiency in Python and hands-on experience with PyTorch/Transformers. Practical experience with Kubernetes and containerization. A solid understanding of evaluation metrics and monitoring processes. Eligible to work in Germany; EU/NATO citizenship preferred.

About the job

Machine Learning Engineer

Company Overview

At Orcrist Technologies, we are pioneers in building the Orcrist Intelligence Platform (OIP), a cutting-edge Kubernetes-based data intelligence system. Our platform is offered as either SaaS or a self-hosted/on-premises solution, including air-gapped deployments. We integrate data processing, machine learning, and artificial intelligence within a modern web application to empower mission-critical clients across both public and private sectors.

Role Overview

As a Machine Learning Engineer, you will spearhead the incubation and validation of new ML initiatives from conception to execution. In this innovative role, you will create adoption-ready prototype vertical slices that encompass data flows, model serving, evaluation, and seamless product integration. You will ensure clear handoff of artifacts to delivery teams so they can effectively productize and maintain them for the long haul.

Key Responsibilities

  • Develop ML prototype vertical slices that bridge data ingestion and processing with inference and end-user product outcomes (such as search functionalities, insights generation, and user experience flows).
  • Establish evaluation harnesses and decision-making artifacts including datasets, baselines, and performance metrics (quality, latency, cost), along with actionable go/no-go recommendations.
  • Package prototypes for seamless adoption: containerize services, specify reproducible deployment strategies, and create comprehensive runbooks/checklists.
  • Collaborate with Research and Data Engineering teams for dataset curation, annotation processes, experiment tracking, and iterative improvements.
  • Ensure operational credibility of prototypes through instrumentation, monitoring, and foundational security/compliance practices (including handling of personally identifiable information and provenance considerations).

Candidate Profile

  • A minimum of 3 years of experience in ML engineering or MLOps, with a proven track record of delivering tangible systems.
  • Proficient in Python and experienced with PyTorch/Transformers; adept at transforming models from notebooks into deployable services.
  • Hands-on experience with Kubernetes and containerization; capable of deploying and troubleshooting within production-like environments, including offline or air-gapped constraints.
  • A strong evaluation mindset and monitoring discipline; effective in clearly articulating trade-offs.
  • Eligibility to work in Germany; EU or NATO citizenship is preferred, and export-control screening applies.

Preferred Qualifications

  • Experience with GPU serving and optimization techniques (e.g., Triton/KServe, ONNX/TensorRT, batching, quantization).
  • Familiarity with streaming and pipeline tools (e.g., Kafka, Ray, Beam/Flink/Spark) and integrations involving search, vector, and graph technologies.
  • Proficiency in the German language (B1+) and/or experience working with regulated or public-sector datasets and workflows.

What We Offer

  • A modern ML stack designed to operate within real-world constraints: Kubernetes, streaming technologies, and hybrid/on-prem/air-gapped deployments.
  • A remote-first work environment in Germany, complemented by regular workshops in Berlin, 30 days of vacation, and support for equipment and learning budgets.
  • High impact: your prototypes will pave the path for groundbreaking solutions.

About Orcrist Technologies

Orcrist Technologies is at the forefront of data intelligence solutions, providing innovative platforms that leverage machine learning and artificial intelligence to serve a diverse range of clients across both public and private sectors.

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