Senior AI/ML Engineer

Location: Cyprus, Poland or Estonia office | Hybrid work model

The Opportunity

Our client is a global financial services group seeking a senior, hands-on engineer to design, build and productionise machine learning models across the business.

You will lead business-critical AI use cases, including client lifetime value, churn prediction and fraud or abuse detection. You will also establish the deployment, monitoring and governance foundations needed to make AI reliable and scalable across the group.

This role suits a pragmatic builder who can own the full ML lifecycle, work through evolving requirements and imperfect data, and explain technical trade-offs to non-technical stakeholders.

Key Responsibilities

Model Development and Delivery

Design, develop, and productionise machine learning models end-to-end (training, validation, deployment, monitoring, retraining), ensuring reliability in real-world environments.

Lead the development of AI use cases such as client lifetime value (CLV), churn prediction, and fraud/abuse detection, with clear alignment to business outcomes and measurable impact.

Drive continuous improvement through feedback loops, monitoring insights, and model retraining strategies, rather than one-off model delivery.

MLOps and Production Reliability

Build and establish robust MLOps practices, including model deployment pipelines, CI/CD, environment promotion (dev/stage/prod), and lifecycle management.

Implement model monitoring frameworks to track performance, data drift, data quality, and business impact, with clear retraining and escalation strategies.

Apply practical experience with model deployment, monitoring, drift detection and retraining to production systems.

Governance and Explainability

Ensure model explainability and transparency using techniques such as SHAP, feature attribution, and other interpretability methods appropriate for business-critical and regulated contexts.

Define and enforce best practices around model governance, documentation, versioning, and auditability, proportional to model risk and business impact.

Cross-Functional Collaboration

Collaborate closely with Data Engineering to ensure high-quality data pipelines, feature engineering, reproducibility, and scalable data foundations.

Work cross-functionally with Product, Risk, Commercial, and other stakeholders to translate business problems into pragmatic AI solutions, balancing speed and robustness.

Mentor team members and promote best practices in production AI, MLOps, and applied machine learning delivery.

Experience Required

5–8+ years of experience building and deploying machine learning models in production environments (not just experimentation).

Strong Python programming skills and solid software engineering fundamentals (testing, code quality, modular design, maintainability).

Strong understanding of machine learning concepts, model evaluation, feature engineering, and practical considerations in production systems (e.g. data leakage, drift, stability).

Hands-on experience with large-scale data processing (Spark / PySpark).

Experience with ML lifecycle tools (MLflow or similar) for experiment tracking, model management, and reproducibility.

Experience building and maintaining CI/CD pipelines (GitHub Actions preferred) for ML or data workflows.

Strong SQL skills and experience working with large, complex datasets in real-world environments.

Proven ability to deliver AI/ML solutions with measurable business impact, not just model performance improvements.

Strong communication skills with the ability to work effectively with both technical and non-technical stakeholders, translating trade-offs clearly.

Ability to operate effectively in environments with evolving requirements, imperfect data, and delivery pressure, balancing MVP speed with production robustness.

Advantageous Experience

Experience in fintech, trading, or financial services environments, particularly where models influence business-critical decisions.

Experience with real-time or streaming ML systems.

Familiarity with modern AI approaches such as LLMs, embeddings, or retrieval-augmented generation (RAG), particularly where applied to business workflows or integrated with structured data.

Experience working in regulated environments and implementing model governance frameworks (e.g. auditability, explainability, approvals, documentation standards).

Experience contributing to team standards, mentoring, or leading applied AI delivery.

Why Consider This Role

You will own applied AI delivery from experimentation through production within a global, growing business. The role offers a remuneration package based on experience, training and development opportunities, team events, and birthday and loyalty benefits.

Hybrid working is available in Cyprus and Poland. In Estonia, the benefits include Stebby sports compensation or Confido health compensation.