About the role

AI/ML Solutions Engineer designing and deploying scalable machine learning models for financial service solutions at Finastra. Collaborating on AI-driven products while ensuring compliance with data privacy standards.

Responsibilities

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.

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.

Flexibility: Enjoy unlimited vacation, based on your location and business priorities.
Hybrid working arrangements, and inclusive policies such as paid time off for voting, bereavement, and sick leave.
Well-being: Access Confidential one-on-one therapy through our Employee Assistance Program, find support from our network of Wellbeing Champions and Gather Groups, and a calendar of monthly events and initiatives designed to help you thrive - Inside and Outside of work.
Medical, life & disability insurance, retirement plan, lifestyle and other benefits*
ESG: Benefit from paid time off for volunteering and donation matching.
DEI: Participate in multiple DE&I groups for open involvement (e.g., Count Me In, Culture@Finastra, Proud@Finastra, Disabilities@Finastra, Women@Finastra).
Career Development: Access online learning and accredited courses through our Skills & Career Navigator tool.
Recognition : Be part of our global recognition program, Finastra Celebrates, and contribute to regular employee surveys to help shape Finastra and foster a culture where everyone is engaged and empowered to perform at their best.