JOB DESCRIPTION SUMMARY

The Machine Learning Engineer role will specialise in maintaining scoring models, production system maintenance and high-availability operations. You'll orchestrate and maintain our AzureML/Databricks-based scoring engine, ensure 99.99% uptime for production models, perform emergency fixes, and manage QA/UAT processes. This role partners with data scientists to operationalize models and data engineers to ensure efficient data flows.

KEY DUTIES & RESPONSIBILITIES

• Production Model Maintenance: Monitor, troubleshoot, and rectify issues in deployed credit scoring models (e.g., score drift, feature misalignment, output anomalies).
• Platform Orchestration: Manage AzureML pipelines & Databricks workflows for model retraining, batch scoring, and real-time inference.
• High-Availability Engineering: Ensure 24/7 uptime of scoring APIs serving banking clients; implement failover systems and load balancing.
• Release Management: Oversee QA/UAT processes for model updates including back-testing, shadow deployments, and canary releases.
• Model Governance: Maintain audit trails for model versions, inputs/outputs, and performance metrics. Support Compliance and Audit with creation of logs when requested.
• Incident Response: Lead troubleshooting of scoring engine failures with SLAs for financial institution clients.
• Infrastructure Optimization: Tune AzureML/Databricks clusters for cost-performance efficiency at scale.
• Vendor Management: Ensure vendor support is completing work as per scope and SLAs. Rectifying any vendor delivery issues.

EDUCATION & SKILLS

1. Educated with at least bachelor’s degree or equivalent in related field
2. Education specialization or master’s degree in computer science, Software Engineering
3. Proficient in English
4. Preferred proficiency in Arabic
5. Proficiency in Russian, Turkish is a plus.
6. In-depth knowledge of React Native and Next JS and related modules, components and libraries
7. Preferred In-depth knowledge of SiteCore or experience integrating with SiteCore
8. Proficiency in Russian, Turkish and Central Asian languages is a plus.

EXPERIENCE & KNOWLEDGE

1. Bachelor's/Master's in Computer Science, Engineering, Data Science, or related field
2. 2+ years in Data Science or Software Engineering experience
3. 2+ years production ML operations experience, MLOps lifecycle management, including monitoring, retraining, and model versioning.
4. Strong problem-solving skills and the ability to resolve issues efficiently.
5. CI/CD for ML systems using tools such as Azure DevOps, GitHub Actions, MLflow, and other similar tools
6. Ability to adapt ML workflows across different cloud environments (Azure, AWS, GCP) as needed.

ABILITIES & SPECIFIC REQUIREMENTS

1. Practical experience in cloud-based ML platforms such as AzureML, Databricks, or equivalent (e.g., SageMaker, Vertex AI), with a preference for Azure.
2. Python/PySpark for model debugging and patching. Working knowledge of Scikit-Learn and NumPy
3. Deep understanding of credit scoring systems: feature engineering, scorecard interpretation, and output validation
4. Credit bureau data structures (tradelines, inquiries, public records)
5. Model risk management (MRM) standards
6. Azure Solutions Architect or MLOps certifications
7. Experience with financial services-grade SLAs (99.9% uptime) and outage management
8. Knowledge of containerization (Docker, Kubernetes, AKS, or similar orchestration tools)
9. Practical experience with Azure Data Factory (ADF) and Azure Data Lake Storage (ADLS) / Azure Blob Storage
10. Knowledge of new and upcoming AI tools
11. This is contained within the earlier requirement of python. If the plan is for this resource
12. Excellent written and verbal communication skills English
13. Strong interpersonal skills with the ability to engage and build relationships
14. Crisis management under pressure
15. Cross-functional collaboration with data science/risk teams
16. Strong organizational skills with the ability to manage multiple tasks and projects simultaneously.
17. Ability to develop and document procedures, roles, and guidelines.
18. Security and Compliance Especially in financial services, mention data security, PII handling, and compliance with regulations