Staff MLOps Engineer (AI/ML Platform)

About the role

We're hiring a Staff MLOps Engineer to own the AI/ML platform at Cint. The immediate focus is supporting the Synthetic Data Platform - models for survey augmentation and respondent profiling - but the role's longer-term remit is broader: Trust Score (our respondent quality and fraud detection model) and other AI/ML initiatives need the same platform capabilities. You'll start by reviewing the current setup and deciding whether to extend it or rebuild parts of it, then build out the shared AI/ML platform from there. The Team You'll report into our Infrastructure and Data Engineering organisation, working in close partnership with the AI/ML team in Prague. This is deliberately a platform-with-feature-focus role: your day-to-day delivery serves the Synthetic Data team's needs, but your architectural remit covers all of Cint's AI/ML workloads.

Responsibilities

• Assess and decide on the current pipeline: Audit the existing AI/ML training and serving setup. Decide what's worth building on and what needs to be rebuilt. Make the call and own the rationale.
• Build the shared AI/ML platform: Training infrastructure, experiment tracking, model registry, serving, monitoring. Built once, used by Synthetic, Trust Score, and whatever comes next.
• Oversee the full ML lifecycle: From data ingestion and feature processing to annotation workflows, ensuring the platform facilitates frictionless, rapid model iteration for Data Scientists.
• Own training infrastructure on Databricks and Unity Catalog: Make training fast, reproducible, and traceable. Lineage matters; reproducibility matters more.
• Model serving: Build the serving layer - low-latency APIs, batch scoring jobs, appropriate caching. Integrate with our Java/Spring services.
• Monitoring and drift: Build the observability our models need - data drift, model drift, accuracy regression, business metrics. Grafana dashboards, Prometheus metrics, clear alerts.
• Cost and performance: ML compute costs add up. Set the patterns for cost-effective training and serving, representing ML infrastructure spend and ROI credibly to finance stakeholders.
• Mentor and multiply: Act as a force multiplier by coaching AI/ML and Infrastructure engineers on engineering best practices. You don't just "do" the work; you set the bar for what "good" looks like.
• Drive AI tooling adoption: Model how AI-native development works for platform teams. Claude Code, agentic workflows, AI-assisted incident response.
• Databricks / Spark Native: Comfortable in Databricks. Unity Catalog experience is a strong plus.
• Kubernetes & Cloud: You've deployed ML workloads on Kubernetes. AWS (EKS) is our environment; familiarity is a plus.
• Be a Polyglot: Python, Scala or Java (for Spark), Kubernetes manifests, Terraform. AWS or GCP. You move between layers without friction.

Requirements

• Deep ML Platform Expertise: You've led ML platform work at a serious scale. You have strong opinions on feature stores, model registries, serving patterns, and what "ML observability" actually means.
• Mature Engineering: You're someone with both a wide and deep background of engineering excellence in a number of disciplines. This is a very senior position in our engineering organisation; setting examples in approach and behaviour is a key trait.
• Systems Architect: You think about the platform as a product with real users (your ML team). You design APIs, write docs, and measure adoption.
• Technical leader: You lead through standards, RFCs, and credibility - not meetings. You've mentored MLOps engineers into senior ICs.
• Pragmatic about buy-vs-build: You know when to adopt a managed service and when to build. You can defend either call to leadership.
• Commercially literate: You can justify platform investment to VP / C-suite and translate business priorities into a roadmap.
• Working at Cint
• Prague-First, Europe-Friendly: Our preferred base is Prague, alongside our existing AI/ML team. Remote work from Germany, Spain or the UK is also possible - these are the markets where we have entities.
• AI-Native Engineering: We're rolling out Claude Code and modern agentic tooling across engineering. You'll use it daily - not as a novelty, but as a force multiplier for the complex problems that matter.
• High Autonomy: We trust our engineers to make sound decisions and own their work end-to-end.
• Global Impact: Your work powers a marketplace used by millions of people worldwide.
• Our Values
• Collaboration is our superpower
• We uncover rich perspectives across the world
• Success happens together
• We deliver across borders.
• Innovation is in our blood
• We're pioneers in our industry
• Our curiosity is insatiable
• We bring the best ideas to life.
• We do what we say
• We're accountable for our work and actions
• Excellence comes as standard
• We're open, honest and kind, always.
• We are caring
• We learn from each other's experiences
• Stop and listen; every opinion matters
• We embrace diversity, equity and inclusion.
• More About Cint
• We're proud to be recognised in Ne

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