Cint
research technology (ResTech)
StaffMLOpsEngineer(AI/MLPlatform)
Neural analysis suggests this role is
optimal for mid candidates.
“Staff MLOps Engineer (AI/ML Platform) at Cint. Skills: MLOps platform ownership, AI/ML platform development, ML lifecycle management, Training infrastructure management, Model serving layer development, Observability for ML models, Kubernetes deployment, Cloud platform experience. Own the AI/ML platform at Cint. Support the Synthetic Data Platform”
Industry & Context.
Assess and decide on the current pipeline; Decide what's worth building on and what needs to be rebuilt; Make the call and own the rationale; Think about the platform as a product with real users
Remote work from Germany, Spain or the UK is also possible
What They're Looking For.
Must Have
Deep ML Platform Expertise, Mature Engineering, Systems Architect, Technical leader, Pragmatic about buy-vs-build, Commercially literate
Nice to Have
Unity Catalog experience is a plus, AWS (EKS) is our familiarity is a plus
What You'll Do.
Own the AI/ML platform at Cint
Support the Synthetic Data Platform
Develop capabilities for Trust Score and other AI/ML initiatives
Review and decide on extending or rebuilding the current AI/ML setup
Build out the shared AI/ML platform
Assess and decide on the current pipeline
Audit the existing AI/ML training and serving setup
Build the shared AI/ML platform (training infrastructure
Oversee the full ML lifecycle
Own training infrastructure on Databricks and Unity Catalog
Build the model serving layer
Build the observability for ML models
Set patterns for cost-effective training and serving
Mentor AI/ML and Infrastructure engineers on engineering best practices
Drive AI tooling adoption
How You'll Work.
Team & Collaboration
Report into Infrastructure and Data Engineering organisation; Work in close partnership with the AI/ML team in Prague; Serve the Synthetic Data team's needs; Architectural remit covers all of Cint's AI/ML workloads; Coach AI/ML and Infrastructure engineers; Act as a force multiplier by coaching engineers
Communication Scope
Represent ML infrastructure spend and ROI credibly to finance stakeholders; Defend buy-vs-build calls to leadership; Justify platform investment to VP / C-suite
Process & Methodology
Own the rationale for decisions, Make the call and own the rationale, Justify platform investment to VP / C-suite, Translate business priorities into a roadmap
Full Job Description
Cint is a pioneer in research technology (ResTech). Our customers use the Cint platform to post questions and get answers from real people to build business strategies, confidently publish research, accurately measure the impact of digital advertising, and more. The Cint platform is built on a programmatic marketplace, which is the world's largest, with nearly 300 million respondents in over 150 countries who consent to sharing their opinions, motivations, and behaviours. 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. ## Qualifications What You'll Do * 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
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