Schemathics is a data-driven technology company operating at the intersection of AI and large-scale consumer engagement. We build and optimize end-to-end digital funnels, processing high volumes of behavioral and event-driven data in real time – and we are placing AI at the core of our workflows.
We are looking for a hands-on MLOps Engineer to design, build, and own a generic deployment platform for ML and AI. You will own the full production lifecycle of our models and pipelines – from packaging and deployment through monitoring, retraining, and incident response – covering both classical ML models and AI-based solutions, including agentic ones.
Requirements
- Hands-on experience deploying and operating AI-based solutions in production, including agentic pipelines, with ownership over evaluation, latency, and cost
- Hands-on experience deploying and maintaining predictive models in production – both classical ML and LLM-based – in batch and online serving modes
- Experience with cloud-based ML platforms and MLOps tooling: experiment tracking, model versioning, model registry, deployment pipelines
- Proven ability to design a generic, reusable deployment platform for ML and AI, rather than one-off deployments
- Experience building CI/CD, monitoring, and observability for production models
- Strong Python and software engineering fundamentals, plus production experience with cloud platforms (GCP / AWS / Azure)
- Creative, self-learning mindset – able to initiate and implement novel MLOps solutions hands-on – and a strong team player
Significant Advantages
- Academic degree in computer science, engineering, statistics, or related fields – big advantage
- Hands-on production experience with Databricks and MLflow – big plus
- Infrastructure-as-code, feature stores, streaming data, or real-time serving at scale
- Familiarity with agent orchestration and AI evaluation frameworks
The position is open for all genders as well as people with disabilities.
Only suitable CVs will be considered.