ML Engineer

About this role

Company Orcrist is building a next generation data intelligence platform using cutting-edge technologies. We’re handling petabyte-scale data with sub-second queries. Our product is a Kubernetes-based platform delivered as B2B SaaS or as a self-hosted on-prem solution, including air-gapped deployments. We enable customers across defense, law enforcement, and enterprise to turn mission-critical data into actionable intelligence by fusing data processing, ML, and intuitive UX.

Role We are looking for a hands-on ML Engineer to build and productionize modern AI capabilities across text, vision, audio, and other applied ML use cases. You will work directly with state-of-the-art and open-source models — testing, evaluating, optimizing, and fine-tuning them for real product use cases. This is not a role focused on training foundation models from scratch or building data pipelines only. You will work closely with Research, Product, and Data Engineering teams to take promising models and ideas from experimentation to reliable, production-ready systems.

What you’ll do Evaluate, compare and integrate open-source models for concrete product and customer use cases. Build and improve LLM-based applications. Extend and improve AI and ML models using inference optimization, evaluation, and fine-tuning where appropriate. Work with NLP, translation, speech-to-text / ASR, image and document understanding, and related applied AI models. Design evaluation frameworks covering model quality, latency, reliability, and cost.

Take models from experimentation into production, including packaging, deployment, monitoring, and iteration. Optimize inference performance and operational cost. Collaborate with Research and Product teams to turn prototypes and experiments into scalable product capabilities. Contribute to modern ML infrastructure and MLOps while remaining hands-on with models and model behaviour. About you 4+ years of experience in Machine Learning Engineering, Applied AI, or a similar hands-on ML role.

Strong Python skills and practical experience with modern ML frameworks and libraries such as PyTorch, Transformers, and Hugging Face. Experience working with LLMs, NLP models, speech models, or other modern generative AI systems. Hands-on experience evaluating and experimenting with existing models rather than only building ML infrastructure. Experience with inference engines like vLLM or SGLang, and platforms like NVIDIA Triton or Ollama.