<p>We are looking for <strong>Senior MLOps Engineer</strong> to join our Risk team. <strong>Risk&nbsp;team </strong>is responsible for automated IT solutions in risk management processes. Within this process we solve the tasks for creating models and inference it on production in real-time with specified SLA, integrate tons of external services and make decisions only on data.<br><br>Our Data Science team develops scoring models including neural network-based approaches, and we need to industrialize the entire ML lifecycle. We are looking for an <strong>MLOps Engineer</strong> to build ML infrastructure from scratch — from automated training pipelines to a unified feature platform serving both DWH research and real-time production inference.<br><br></p>
<div><strong>Challenges That Await You</strong></div>
<ul>
<li>Conduct evaluation of Feature Store / Feature Registry solutions (Feast, Tecton, or custom), prepare a recommendation, design the architecture and feature lifecycle process (experiment → stable → production). Lead the implementation by engineering and platform teams</li>
<li>Design, build, and own ML training and deployment pipelines: experiment tracking, model registry, CI/CD for models, packaging and handoff to production. Select the platform (MLflow or alternatives), establish versioning and validation standards, and evolve the infrastructure continuously</li>
<li>Select tooling and set up monitoring of model quality (drift, degradation) and feature health (freshness, data quality) in collaboration with the DS team. Define the alerting and response process</li>
</ul>
<div><strong>What Makes You a Great Fit</strong></div>
<ul>
<li>3+ years of experience in ML Engineering, Data Engineering, or DevOps with hands-on involvement in deploying and maintaining ML systems in production</li>
<li>Experience building ML training pipelines with experiment tracking and model registry (MLflow, W&amp;B, or similar)</li>
<li>Understanding of feature store concepts and train-serve consistency challenges</li>
<li>Strong Python skills; understanding of ML frameworks enough to package, serve, and debug models</li>
<li>Experience with Docker and ML pipeline orchestration (Kubeflow, Argo Workflows, Metaflow, or similar)</li>
<li>Solid SQL skills and understanding of data warehouse architecture</li>
<li>B1 or higher English level for effective communication with an international team</li>
</ul>
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<div class="tn-atom"><strong>Our ways of working:</strong></div>
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<li><strong>Innovative Spirit:&nbsp;</strong>A commitment to creativity and groundbreaking solutions</li>
<li><strong>Honest Feedback:</strong>&nbsp;valuing open, transparent communication</li>
<li><strong>Supportive Team:&nbsp;</strong>a strong, collaborative community</li>
<li><strong>Celebrating Achievements:&nbsp;</strong>recognizing our wins together</li>
<li><strong>High-Tech Environment:</strong>&nbsp;a team full of smart and revolutionary people who date to challenge the status quo of incumbent finances</li>
</ul>
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<div class="tn-atom"><strong>Our benefits:</strong></div>
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<li data-list="bullet"><strong data-stringify-type="bold">Relocation support</strong> to one of our hubs — Cyprus, Serbia, Georgia or Kazakhstan — with assistance for the employee and their family</li>
<li><strong>Flexible work&nbsp;</strong>from one of our offices or remote</li>
<li><strong>Healthcare</strong>&nbsp;<strong>Coverage</strong></li>
<li data-list="bullet"><strong>Education Budget:</strong>&nbsp;Language lessons, professional training and certifications</li>
<li data-list="bullet"><strong>Wellness Budget:</strong>&nbsp;Mental health and fitness activity reimbursements</li>
<li data-list="bullet"><strong>Vacation policy:</strong>&nbsp;20 days of annual leave and paid sick leave</li>
</ul>
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