Lead Machine Learning Engineer
Lead ML Engineer at Experian in Sofia: hands-on technical leadership to industrialize credit Scores & Attributes across international markets — designing reusable ML/data architectures, production pipelines and platforms (Python, Spark, Databricks, AWS/Azure, Kubernetes) while mentoring engineers and data scientists.
- Compensation
- Not disclosed
- Location
- Sofia, bg
- Work setup
- Hybrid
- Seniority
- Lead
- Employment
- Not specified
- Experience
- Not specified
- Category
- Ml / AI
- Posted
Skills
Must have
- Software engineering fundamentals: OOP, data structures, algorithms, design patterns, testing
- Hands-on Python with data/analytical ecosystem (Pandas, NumPy)
- Experience developing production-grade data or ML applications, not just prototypes/notebooks
- SDLC, Agile practices, code review, version control
- Applied ML and statistics: model evaluation, feature engineering, overfitting, cross-validation, train/validation/test
- Large-scale data processing with Apache Spark / PySpark and Spark SQL
- Experience with Databricks or comparable modern data/ML platform
- Understanding of Delta Lake, distributed data processing, data-quality controls
- Developing reusable features/attributes for ML or analytical applications
- Practical experience with at least one major cloud platform (AWS or Azure)
- Core cloud concepts: compute, object storage, networking, IAM, security, managed data services
- AWS services such as S3, EC2, Lambda, EKS, SageMaker, OpenSearch, managed Kafka or equivalents
- Design cloud solutions for performance, scalability, reliability, security and cost
- Experience with CI/CD pipelines (Jenkins desirable)
- Infrastructure as Code, e.g. Terraform
- Hands-on Docker and Kubernetes, container-based deployment
- Comfortable working in Linux environments
- Troubleshooting production systems; identifying performance/scalability/reliability bottlenecks
- Monitoring, logging and operational practices for production data/ML services
Nice to have
- Designing APIs/services for data or ML capabilities; FastAPI or similar desirable
- MLOps: ML lifecycle, model/feature tracking, deployment
- Azure services: Blob Storage, VMs, networking, Entra ID, Databricks, Azure Data Factory
- Desirable: Apigee, SSO/Okta, legacy workload migration, DS/analytics platform tooling, regulated financial services experience
Full description
We are looking for a Lead ML Engineer to provide technical leadership for the development and industrialisation of Scores & Attributes across multiple international markets. You will report to the Head of Analytics Product Development - EMEA and APAC. You will work at the intersection of machine learning, data engineering and platform engineering, helping transform analytical innovation and large-scale data assets into reusable, governed and production-ready capabilities.
You will lead the technical journey from data exploration and attribute development through testing, governance, deployment and production execution. An important part of the role will design reusable architectures and integration patterns that allow these capabilities to grow across markets and existing technology environments.
This is a hands-on technical leadership role: you will combine architecture and engineering leadership with direct contribution to complex implementations. You will mentor engineers and data scientists and establishing standards that help teams to operate independently.
What you will do:ML Engineering & Attribute Industrialisation
Lead the design and evolution of capabilities for developing, calculating, testing, governing, deploying and operating data attributes at scale. Build reusable frameworks, tooling and automation that allow attributes to move from experimentation and prototyping into governed, production-ready services. Ensure solutions meet requirements for performance, scalability, reliability and maintainability.
Technical Architecture & Platform Integration
Shape the end-to-end technical architecture and engineering patterns supporting the attribute lifecycle - from development in analytical environments through governance and deployment to production execution. Design integration patterns that allow attribute services to work with existing data and bureau platforms while minimising implementation effort for consuming systems. Lead performance and scalability validation and resolve complex cross-platform integration challenges.
Data & Attribute Innovation
Evaluate new data sources and methodologies, quantifying their incremental predictive and analytical value. Develop and implement approaches for demonstrating the value of Experian data assets and translating them into new attributes and capabilities. Explore technical approaches such as synthetic data to support analytical experimentation, product development and data propositions.
Data Engineering & Asset Onboarding
Design reusable approaches for onboarding new data assets and existing attribute libraries, including data ingestion, transformation and ETL pipelines. Establish data-quality controls and engineering practices. Develop approaches for migrating legacy attributes and data processes onto strategic platforms while maintaining business continuity.