Description review
MLOps Senior [Risk Team]
Banco Plata, S.A., Institución de Banca Múltiple · Cyprus, Georgia, Kazakhstan, Serbia · back to the listing
HR standards
48/100
poor
Title ↔ description
77/100
solid
Reads as
Machine Learning Engineer
100% confident
What this role officially is
data scientist — ESCO, the EU occupation classification
Data scientists find and interpret rich data sources, manage large amounts of data, merge data sources, ensure consistency of data-sets, and create visualisations to aid in understanding data. They build mathematical models using data, present and communicate data insights and findings to specialists and scientists in their team and if required, to a non-expert audience, and recommend ways to apply the data.
Also known as: data scientists, data engineer, research data scientist, data expert, data research scientist
How others title the same work
Large employers
- Applied AI Engineer Automattic
- Machine Learning Engineer, CX Intelligence Coinbase
- AI Engineer - FDE (Forward Deployed Engineer) Databricks
- AI Engineer - FDE (Forward Deployed Engineer) - U.S. Federal Sector Databricks
- Senior AI Engineer – Notebooks Datadog
Startups
- Attendi | Medior Machine Learning Engineer | Amsterdam, Netherlands | ONSITE (hybrid) | €6,000 - €7,000 per month | Full-time (80–100%, ~4–5 days/week) | Visa sponsorship + 30% ruling possible Attendi
- Member of Technical Staff (applied) Anthrogen
- Aptura AI | Full-Time | MTS (Applied AI), MTS (SWE / Product) | London | ONSITE / HYBRID Aptura AI
- Arcforma AI (arcforma.ai) | AI Engineer (Marketing / Construction / Arcforma AI (arcforma.ai)
- Staff AI Engineer - Agent Architecture & Behavior Artisan
What the listing never says
- 21 bullet points. Long requirement lists deter qualified candidates, who read them as hard gates. Scope clarity
- No section describes what the person would actually do. Scope clarity
- No pay range published. Candidates cannot tell whether applying is worth their time. Pay transparency
The listing, marked up
Nothing in the wording of this listing tripped a check. The scores above still judge how complete and coherent it is.
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
•
HealthcareCoverage
•
Education Budget: Language lessons, professional training and certifications
•
Wellness Budget: Mental health and fitness activity reimbursements
•
Vacation policy: 20 days of annual leave and paid sick leave
Originally posted on Himalayas
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
•
HealthcareCoverage
•
Education Budget: Language lessons, professional training and certifications
•
Wellness Budget: Mental health and fitness activity reimbursements
•
Vacation policy: 20 days of annual leave and paid sick leave
Originally posted on Himalayas