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 Engineer, AI/ML Platform, ML lifecycle, Databricks, Kubernetes, AWS, Python, Scala, Java, Terraform. Assess and decide on the current pipeline. Audit the existing AI/ML training and serving setup”
What You'll Achieve.
Build out the shared AI/ML platform from there; Make training fast, reproducible, and traceable; Ensure the platform facilitates frictionless, rapid model iteration for Data Scientists; Represent ML infrastructure spend and ROI credibly to finance stakeholders; Act as a force multiplier by coaching AI/ML and Infrastructure engineers on engineering best practices; You don’t just "do" the you set the bar for what "good" looks like; Model how AI-native development works for platform teams; You design APIs, write docs, and measure adoption
Industry & Context.
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; Pragmatic about buy-vs-build; You know when to adopt a managed service and when to build
Remote work from Germany, Spain or the UK is also possible — these are the markets where we have entities.
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, Kubernetes a plus
What You'll Do.
Assess and decide on the current pipeline
Audit the existing AI/ML training and serving setup
Build the shared AI/ML platform
Oversee the full ML lifecycle
Own training infrastructure on Databricks and Unity Catalog
Build the serving layer
Build the observability our models need
Set the patterns for cost-effective training and serving
Drive AI tooling adoption
How You'll Work.
Team & Collaboration
working in close partnership with the AI/ML team in Prague; your day-to-day delivery serves the Synthetic Data team's needs; your architectural remit covers all of Cint's AI/ML workloads; coaching AI/ML and Infrastructure engineers on engineering best practices; You think about the platform as a product with real users (your ML team); Collaboration is our superpower; We uncover rich perspectives across the world; Success happens together; We deliver across borders
Communication Scope
represent ML infrastructure spend and ROI credibly to finance stakeholders; You lead through standards, RFCs, and credibility — not meetings; You can defend either call to leadership; You can justify platform investment to VP / C-suite and translate business priorities into a roadmap
Process & Methodology
Make the call and own the rationale, Own training infrastructure on Databricks and Unity Catalog, Make training fast, reproducible, and traceable, Build the serving layer, Own the rationale, Make the call
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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