Trainer at WeAndTheMany facilitating learning by sharing experiences and creating interactive sessions. Engaging with students to enhance their skills and knowledge through dynamic teaching methods.
About the role
- AI/ML Engineer developing machine learning systems for digital banking at TymeX. Focused on building trustworthy, explainable systems that enhance customer interaction and reliability.
Responsibilities
- As an AI/ML Engineer, you'll work across the full ML lifecycle, from model design and experimentation to large-scale deployment and performance optimization, within a distributed, microservices-based, and cloud-native environment.
- Design, build, and optimize AI/ML pipelines for real-time and batch inference, leveraging modern MLOps practices.
- Collaborate with data engineers and software developers to integrate models into TymeX's banking platform, ensuring reliability, monitoring, and version control.
- Research, prototype, and productionize models in areas such as credit scoring, fraud detection, transaction classification, personalization, and conversational AI.
- Implement robust model evaluation, A/B testing, and drift detection frameworks to ensure accuracy and stability over time.
- Contribute to internal frameworks and libraries to standardize ML development workflows across teams.
- Explore and evaluate emerging techniques in LLMs, Generative AI, and reinforcement learning applicable to TymeX's ecosystem.
- Mentor junior engineers and collaborate closely with product and infrastructure teams to ensure model readiness for global scale.
Requirements
- 5+ years of hands-on experience in machine learning engineering, data science, or related software development roles.
- Proven experience deploying and maintaining ML models in production environments (preferably in fintech, e-commerce, or large-scale consumer products).
- Strong proficiency in Python and core ML libraries (TensorFlow, PyTorch, Scikit-learn, XGBoost, etc.).
- Solid understanding of algorithms, data structures, and distributed computing concepts.
- Experience with MLOps tools (e.g., MLflow, Kubeflow, Airflow, Docker, Kubernetes, CI/CD pipelines).
- Familiarity with data engineering stacks such as Spark, Kafka, and cloud data warehouses (e.g., BigQuery, Redshift).
- Strong understanding of model monitoring, versioning, and observability in live systems.
- Experience with cloud environments (AWS, GCP, or Azure) and IaC (Terraform, CloudFormation) is a plus.
- Familiarity with secure data handling and compliance within regulated environments (e.g., banking or financial data) is advantageous.
- A system-thinking approach to ML development, viewing models as part of a continuous delivery ecosystem.
- Curiosity for applied AI research balanced with pragmatism for production constraints.
- Strong communication skills and ability to collaborate in cross-functional and multi-country teams.
Benefits
- Opportunity to build large-scale AI systems that power global digital banking across multiple markets.
- Work in a cloud-native, event-driven, and API-first architecture with cutting-edge technologies.
- Join a team where AI is not an experiment; it's a core part of the platform strategy.
- Hybrid working model, strong engineering culture, and continuous learning environment (LLMs, MLOps, data observability, and more).
- Be part of a global technology organization with shared ownership and impact.
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