MLOps Senior [Risk Team]
- Salary not listed
- Remote-friendly · fits your timezone
- Posted 4 weeks ago · via Greenhouse
Build ML infrastructure from scratch for real-time risk models
Build ML infrastructure from scratch, including automated training pipelines and a unified feature platform serving both DWH research and real-time production inference. Conduct evaluation of Feature Store solutions, design architecture and feature lifecycle process, and lead implementation. Design, build, and own ML training and deployment pipelines with experiment tracking, model registry, and CI/CD for models.
Why This Role?
Lead the implementation of ML infrastructure from scratch for the Risk team
Key Responsibilities
- Evaluate Feature Store solutions like Feast or Tecton and prepare recommendations
- Design architecture and feature lifecycle process from experiment to production
- Build and own ML training and deployment pipelines with experiment tracking and model registry
- Select and implement MLflow or similar platforms for model versioning and validation
- Set up monitoring for model quality and feature health including drift detection and alerting
- Collaborate with Data Science team to define alerting and response processes
Requirements
- 3+ years of experience in ML Engineering, Data Engineering, or DevOps with ML systems deployment
- Experience building ML training pipelines with experiment tracking and model registry (MLflow, W&B, or similar)
- Understanding of feature store concepts and train-serve consistency challenges
- Strong Python skills and ability to package, serve, and debug ML models
- Experience with Docker and ML pipeline orchestration tools (Kubeflow, Argo Workflows, Metaflow, or similar)
- Solid SQL skills and understanding of data warehouse architecture
Required Skills
Indonesia Context
- Working Hours Overlap:
- Flexible — work your own hours
View Original Description from Greenhouse BoardsShow more
Original description from Greenhouse Boards
<p>We are looking for <strong>Senior MLOps Engineer</strong> to join our Risk team. <strong>Risk 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&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> <div class="t396__elem tn-elem tn-elem__8559900061715530713485" data-elem-id="1715530713485" data-elem-type="text" data-field-top-value="1024" data-field-left-value="271" data-field-height-value="20" data-field-width-value="656" data-field-axisy-value="top" data-field-axisx-value="left" data-field-container-value="grid" data-field-topunits-value="px" data-field-leftunits-value="px" data-field-heightunits-value="px" data-field-widthunits-value="px" data-field-textfit-value="autoheight" data-field-top-res-320-value="1715" data-field-left-res-320-value="12" data-field-width-res-320-value="195" data-field-container-res-320-value="grid" data-field-top-res-480-value="1638" data-field-left-res-480-value="80" data-field-width-res-480-value="318" data-field-top-res-640-value="1204" data-field-left-res-640-value="65" data-field-width-res-640-value="307" data-field-top-res-960-value="1051" data-field-left-res-960-value="153" data-fields="top,left,width,height,container,axisx,axisy,widthunits,leftunits,topunits"> <div class="tn-atom"><strong>Our ways of working:</strong></div> </div> <div class="t396__elem tn-elem tn-elem__8559900061715530713504" data-elem-id="1715530713504" data-elem-type="text" data-field-top-value="1060" data-field-left-value="271" data-field-height-value="182" data-field-width-value="598" data-field-axisy-value="top" data-field-axisx-value="left" data-field-container-value="grid" data-field-topunits-value="px" data-field-leftunits-value="px" data-field-heightunits-value="px" data-field-widthunits-value="px" data-field-textfit-value="autoheight" data-field-top-res-320-value="1747" data-field-left-res-320-value="10" data-field-width-res-320-value="303" data-field-container-res-320-value="grid" data-field-top-res-480-value="1670" data-field-left-res-480-value="80" data-field-height-res-480-value="338" data-field-width-res-480-value="321" data-field-container-res-480-value="grid" data-field-top-res-640-value="1239" data-field-left-res-640-value="65" data-field-width-res-640-value="489" data-field-top-res-960-value="1087" data-field-left-res-960-value="150" data-fields="top,left,width,height,container,axisx,axisy,widthunits,leftunits,topunits"> <div class="tn-atom"> <ul> <li><strong>Innovative Spirit: </strong>A commitment to creativity and groundbreaking solutions</li> <li><strong>Honest Feedback:</strong> valuing open, transparent communication</li> <li><strong>Supportive Team: </strong>a strong, collaborative community</li> <li><strong>Celebrating Achievements: </strong>recognizing our wins together</li> <li><strong>High-Tech Environment:</strong> a team full of smart and revolutionary people who date to challenge the status quo of incumbent finances</li> </ul> </div> </div> <div class="t396__elem tn-elem tn-elem__8559900061715530713509" data-elem-id="1715530713509" data-elem-type="text" data-field-top-value="1274" data-field-left-value="271" data-field-height-value="20" data-field-width-value="656" data-field-axisy-value="top" data-field-axisx-value="left" data-field-container-value="grid" data-field-topunits-value="px" data-field-leftunits-value="px" data-field-heightunits-value="px" data-field-widthunits-value="px" data-field-textfit-value="autoheight" data-field-top-res-320-value="2110" data-field-left-res-320-value="11" data-field-width-res-320-value="210" data-field-container-res-320-value="grid" data-field-top-res-480-value="2032" data-field-left-res-480-value="80" data-field-width-res-480-value="318" data-field-top-res-640-value="1530" data-field-left-res-640-value="69" data-field-width-res-640-value="307" data-field-top-res-960-value="1300" data-field-left-res-960-value="154" data-fields="top,left,width,height,container,axisx,axisy,widthunits,leftunits,topunits"> <div class="tn-atom"><strong>Our benefits:</strong></div> </div> <div class="t396__elem tn-elem tn-elem__8559900061715530713512" data-elem-id="1715530713512" data-elem-type="text" data-field-top-value="1310" data-field-left-value="271" data-field-height-value="208" data-field-width-value="634" data-field-axisy-value="top" data-field-axisx-value="left" data-field-container-value="grid" data-field-topunits-value="px" data-field-leftunits-value="px" data-field-heightunits-value="px" data-field-widthunits-value="px" data-field-textfit-value="autoheight" data-field-top-res-320-value="2142" data-field-left-res-320-value="10" data-field-width-res-320-value="308" data-field-container-res-320-value="grid" data-field-top-res-480-value="2064" data-field-left-res-480-value="80" data-field-height-res-480-value="338" data-field-width-res-480-value="333" data-field-container-res-480-value="grid" data-field-top-res-640-value="1565" data-field-left-res-640-value="65" data-field-width-res-640-value="487" data-field-top-res-960-value="1336" data-field-left-res-960-value="150" data-field-width-res-960-value="582" data-fields="top,left,width,height,container,axisx,axisy,widthunits,leftunits,topunits"> <div class="tn-atom"> <ul> <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 </strong>from one of our offices or remote</li> <li><strong>Healthcare</strong> <strong>Coverage</strong></li> <li data-list="bullet"><strong>Education Budget:</strong> Language lessons, professional training and certifications</li> <li data-list="bullet"><strong>Wellness Budget:</strong> Mental health and fitness activity reimbursements</li> <li data-list="bullet"><strong>Vacation policy:</strong> 20 days of annual leave and paid sick leave</li> </ul> </div> </div>
Salary Context
Similar Engineering roles on LokerDollar pay around $185k/yr (range $48k–582.4k/yr, n=274 active listings).
Hiring at Plata Card
Plata Card has 28 other active roles on LokerDollar and has been hiring here since Oct 13, 2026 — across Engineering, Data & Analytics.
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Market context
- ESTIMATEEstimated pay is 28% below the role median of $185,000/year (n=274 pay-disclosing listings).
- VERIFIEDPlata Card: 30 postings in the last 3 months, 30 all-time on LokerDollar.
- VERIFIEDCompany first seen Oct 13, 2026.
- VERIFIEDThis listing first seen Oct 13, 2026.
- VERIFIEDLast verified live Oct 13, 2026.
Frequently asked questions
- Is MLOps Senior [Risk Team] at Plata Card a remote job?
- Yes. MLOps Senior [Risk Team] at Plata Card is a fully remote role open to candidates worldwide.
- What type of employment is MLOps Senior [Risk Team] at Plata Card?
- This is a full time position.
- How do I apply?
- Click the "Apply" button on this page to go to the official application at Plata Card.
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