Staff MLOps Engineer (AI/ML Platform)

Remote from: UK
Annual salary: Undisclosed
Salary information is not provided for this position. Check our Salary Directory to estimate the average compensation for similar roles.
Department: Software Engineering
Employment type: Full Time,
Job posted: 14 May 2026
Apply before: 14 Jun 2026
Experience level: Senior
Views / Applies: 21 / 4

About Cint

The World's Largest Global Research Marketplace connecting your research questions to the right people.

Market Research
1998

AI Summary

Cint, a research technology company, is hiring a Staff MLOps Engineer to own and build their AI/ML platform. The role initially supports the Synthetic Data Platform, with future scope including Trust Score and other AI initiatives. The engineer will assess the current setup, build shared platform capabilities (training, serving, monitoring), and work closely with the AI/ML team in Prague. This senior position requires deep ML platform expertise, systems architecture skills, and technical leadership. The role reports into Infrastructure and Data Engineering, focusing on platform-as-product delivery.

Job Complexity

Easy Hard

AI Insight This is a highly senior role requiring deep expertise in ML platform engineering, systems architecture, and technical leadership across multiple disciplines. The engineer must make critical architectural decisions and mentor others, indicating the highest level of difficulty.

Salary Analysis

AI Insight The salary for this role is not specified, but based on market data for a Staff MLOps Engineer at a senior level, the median is estimated at $210,000. This is competitive for the role's high seniority and specialized skill set.

Key Skills

MLOps AI Platform Kubernetes Databricks Python AWS Model Serving Infrastructure as Code Machine Learning Technical Leadership

Cover Letter Sample

Dear Hiring Team,

I am writing to express my strong interest in the Staff MLOps Engineer position at Cint. With extensive experience building and scaling AI/ML platforms, I am excited about the opportunity to own and evolve the platform that powers your Synthetic Data and Trust Score models. My background includes leading ML platform initiatives, designing training and serving infrastructure, and implementing robust monitoring and cost optimization strategies.

I have deep expertise in Databricks, Kubernetes, and cloud environments, and I am passionate about creating platforms that enable data scientists to iterate rapidly and reliably. At my previous role, I successfully rebuilt an ML platform that reduced model deployment time by 60% and improved cost efficiency by 30%. I am also a strong advocate for engineering best practices and enjoy mentoring teams to raise the bar on quality.

I am particularly drawn to Cint's mission of powering research technology and the chance to work on a platform that serves millions of respondents globally. I am confident that my technical leadership and hands-on skills would make a significant impact on your AI/ML initiatives. I look forward to the possibility of discussing how I can contribute to your team.

Sincerely,
[Your Name]

Possible Interview Questions

Can you describe a time when you had to decide between extending an existing ML platform and rebuilding parts of it? What factors influenced your decision?

At my previous company, we had a legacy ML training pipeline that was difficult to maintain and scale. I audited the system and found that while the data ingestion layer was solid, the model registry and serving components were outdated. I decided to rebuild the serving layer using Kubernetes and gRPC, while extending the training pipeline to integrate with a new experiment tracking tool. The decision was based on cost-benefit analysis: rebuilding the serving layer would reduce latency by 40% and improve reliability, while extending the training pipeline was faster and preserved existing investments.

How would you design a shared AI/ML platform to support multiple use cases like Synthetic Data and Trust Score?

I would start by defining clear abstractions for training, serving, and monitoring that can be reused across teams. The platform would include a feature store for consistent data ingestion, a model registry with versioning and lineage, and a serving layer supporting both real-time APIs and batch scoring. Monitoring would cover data drift, model drift, and business metrics, with standardized dashboards. I would also implement cost tracking per workload to enable chargeback and optimization. The key is to build with extensibility in mind, using APIs and documentation so teams can adopt with minimal friction.

Walk me through how you would set up monitoring for an ML model in production. What metrics would you track and why?

I would monitor both infrastructure and model-specific metrics. Infrastructure metrics include latency, throughput, error rates, and resource utilization (CPU, memory, GPU). Model-specific metrics include prediction distribution, feature drift (comparing incoming data to training data), and model drift (accuracy or other performance metrics over time). I would also track business metrics like conversion rate or user satisfaction if applicable. Alerts would be set up for significant deviations, with dashboards in Grafana and Prometheus for real-time visibility. This ensures we catch issues like data pipeline changes or model degradation early.

How do you approach cost optimization for ML compute workloads, and how would you communicate ROI to finance stakeholders?

I start by tagging all resources by team and workload to track spend. For training, I use spot instances where possible, right-size instances, and implement auto-scaling for batch jobs. For serving, I use autoscaling and caching to reduce costs. I also set budgets and alerts. To communicate ROI, I calculate the value generated by the models (e.g., increased revenue or cost savings) and compare it to compute costs. I present this in regular reviews, showing trends and optimization opportunities. This builds trust and justifies continued investment.

Describe your experience with Databricks and Unity Catalog. How would you leverage them in this role?

I have used Databricks extensively for data engineering and ML workloads, including setting up clusters, managing libraries, and using Delta Lake for reliable data pipelines. Unity Catalog provides centralized metadata management, which is crucial for data discovery, lineage, and governance. In this role, I would use Databricks for training infrastructure, ensuring reproducibility and traceability. Unity Catalog would manage data assets, feature tables, and model artifacts, enabling a unified view across the organization. I would also set up access controls and audit logs to meet compliance requirements.

Company 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.

Job Description

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 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.

Who You Are

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.

Additional Information

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 Newsweek’s 2025 Global Top 100 Most Loved Workplaces®, reflecting our commitment to a culture of trust, respect, and employee growth.

In June 2021, Cint acquired Berlin-based GapFish – the world’s largest ISO certified online panel community in the DACH region – and in January 2022, completed the acquisition of US-based Lucid – a programmatic research technology platform that provides access to first-party survey data in over 110 countries.

Cint Group AB (publ), listed on Nasdaq Stockholm, this growth has made Cint a strong global platform with teams across its many global offices, including Stockholm, London, New York, New Orleans, Singapore, Tokyo and Sydney. (www.cint.com)

Additionally, in a world of AI, we want our candidates to understand our approach to the use of AI during the interview and hiring process, so we’d appreciate you reading our AI usage guide.

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