MLOps Senior [Risk Team]

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Work from anywhere3+ years requiredPublished 12 February 2026Ghost risk: high

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First recorded by Psst: 12 October 2026. Your first 3 jobs are free to apply to (the first right away, 2 more with your email). After that, the company and the apply link are for Psst members, from $4.99 for 7 days.

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The 20-second read

Experience
3+ years required
Where you can live
Work from anywhere
Languages
English
Skills and tools
sql, python, docker, ci/cd, sla

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Job details

Where you can work
Work from anywhere
Employment type
Not stated by the employer
Advertised pay
Not stated by the employer
Experience
3+ years required
Employer publication date
12 February 2026
First recorded by Psst
12 October 2026
Last checked at the source
10 November 2026

About the job

We are looking for Senior MLOps Engineer to join our Risk team. Risk team 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.

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 MLOps Engineer to build ML infrastructure from scratch — from automated training pipelines to a unified feature platform serving both DWH research and real-time production inference.

Challenges That Await You

  • 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
  • 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
  • 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

What Makes You a Great Fit

  • 3+ years of experience in ML Engineering, Data Engineering, or DevOps with hands-on involvement in deploying and maintaining ML systems in production
  • 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; understanding of ML frameworks enough to package, serve, and debug models
  • Experience with Docker and ML pipeline orchestration (Kubeflow, Argo Workflows, Metaflow, or similar)
  • Solid SQL skills and understanding of data warehouse architecture
  • B1 or higher English level for effective communication with an international team

Our ways of working

  • Innovative Spirit:  A commitment to creativity and groundbreaking solutions
  • Honest Feedback:  valuing open, transparent communication
  • Supportive Team:  a strong, collaborative community
  • Celebrating Achievements:  recognizing our wins together
  • High-Tech Environment:  a team full of smart and revolutionary people who date to challenge the status quo of incumbent finances

Our benefits

  • Relocation support to one of our hubs — Cyprus, Serbia, Georgia or Kazakhstan — with assistance for the employee and their family
  • Flexible work  from one of our offices or remote
  • Healthcare   Coverage
  • Education Budget:  Language lessons, professional training and certifications
  • Wellness Budget:  Mental health and fitness activity reimbursements
  • Vacation Policy: Annual leave and paid sick leave as per applicable policies

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Ghost Job Detector: Ghost risk: high
  • Open 9 months
  • No pay listed
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