Cint

research technology (ResTech)

StaffMLOpsEngineer(AI/MLPlatform)

Spain FULL TIME Remote Friendly
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for mid candidates.

The Brief

“Staff MLOps Engineer (AI/ML Platform) at Cint. Skills: MLOps, AI/ML Platform Engineering, Kubernetes, Databricks, AWS, Python, Scala, Java, Terraform. Assess and decide on the current AI/ML training and serving setup. Build the shared AI/ML platform (training infrastructure, experiment tracking, model registry, serving, monitoring)”

What You'll Achieve.

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; Set the bar for what 'good' looks like in engineering; Model how AI-native development works for platform teams; Design APIs, write docs, and measure adoption

Industry & Context.

research technology (ResTech)
Problems you'll solve

Solve complex problems

What They're Looking For.

Must Have

Deep ML Platform Expertise, Mature Engineering, Systems Architect, Technical leader, Pragmatic about buy-vs-build, Commercially literate, Databricks / Spark Native, Kubernetes & Cloud, Be a Polyglot

Nice to Have

Unity Catalog experience, AWS (EKS) familiarity

What You'll Do.

Assess and decide on the current AI/ML training and serving setup

Build the shared AI/ML platform (training infrastructure

Oversee the full ML lifecycle (data ingestion

annotation workflows)

Own training infrastructure on Databricks and Unity Catalog

Build the model serving layer (low-latency APIs

Build model observability (data drift

Set patterns for cost-effective training and serving

Mentor AI/ML and Infrastructure engineers on engineering best practices

Drive AI tooling adoption (Claude Code

AI-assisted incident response)

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; Collaborate across global offices

Communication Scope

Defend decisions to leadership; Justify platform investment to VP / C-suite

Process & Methodology

Own the rationale for platform decisions, Own the AI/ML platform end-to-end, 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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