Senior AI / ML Solutions Engineer

What will you contribute?

Finastra’s Global Data Office is responsible for the strategy, governance, and enablement of data assets across 130+ countries globally to unlock innovation for our business units and 8000+ customers, including 90% of the world’s top banks. Reporting to the Global Head of Data Governance, this position will play a key role on the Enterprise Data Governance team, supporting the implementation and ongoing development of data governance initiatives across Finastra. It involves both business and technical responsibilities, with an emphasis on assisting in the development, configuration, and maintenance of Finastra’s enterprise data governance systems. The purpose is to maintain the security, accessibility, quality, usability, and integrity of critical data assets. The successful candidate will support the Global Data Office in advancing Finastra’s data governance strategies and solutions aimed at enhancing the technical infrastructure that underpins our governance framework. This team will help drive a global transformation Finastra’s data governance program, which will not only meet and exceed security, regulatory and compliance requirements, but also accelerate innovation in the development of data, analytic and AI products, unlocking new opportunities for monetization.

Responsibilities & Deliverables

Design, develop, and deploy scalable machine learning models to solve complex business problems in the financial services domain.

Collaborate with data scientists, data engineers, and business stakeholders to understand requirements and translate them into technical solutions.

Build and maintain ML pipelines for data preprocessing, model training, evaluation, and deployment using MLOps best practices.

Conduct exploratory data analysis and feature engineering to improve model performance and interpretability.

Monitor model performance in production and implement retraining strategies to ensure accuracy and relevance over time.

Contribute to the development of AI-driven products and services, including fraud detection, credit scoring, customer segmentation, and predictive analytics.

Ensure compliance with data privacy, security, and regulatory standards (e.g., GDPR, Fair Lending).

Document model development processes, assumptions, and performance metrics for transparency and audit readiness.

Stay current with advancements in AI / ML technologies and recommend innovative solutions to enhance business capabilities.

Required Skills & Experience

3+ years of experience in machine learning, data science, or AI engineering.

5+ years’ experience supporting global data governance and data management initiatives, preferably in the banking, fintech or other highly regulated industries.

Proficiency in Python and ML libraries such as scikit-learn, TensorFlow, PyTorch, or XGBoost.

Strong understanding of supervised and unsupervised learning, model evaluation techniques, and statistical analysis.

Experience with cloud platforms

(e.g., Azure ML, AWS SageMaker, or GCP AI Platform) and containerization tools (e.g., Docker, Kubernetes).

Familiarity with MLOps tools and practices for versioning, CI / CD, and model monitoring.

Experience working with large-scale datasets and distributed data processing frameworks (e.g., Spark, Databricks).

Knowledge of financial services use cases and key performance indicators (e.g., risk modeling, transaction classification, customer behavior analytics).

Excellent problem-solving and communication skills, with the ability to explain complex models to non-technical stakeholders.

Qualifications

Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, or a related field; PhD is a plus.

Strong understanding of data governance principles, metadata management, data quality, and data management frameworks.

Proven ability to support strategic implementation, manage change, and deliver results under tight deadlines.

Excellent interpersonal skills with a demonstrated ability to collaborate effectively with cross-functional stakeholders, including product, legal, privacy, risk, and security teams.

Exceptional written and verbal communication skills in English.

Professional certifications in data governance (e.g., CDMP, ADGP, IGP) are advantageous.