Machine Learning Engineer designing and implementing AI systems focused on Japanese language challenges at Woven by Toyota. Involves technical R&D, system design, and collaboration with cross - functional teams.
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.
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
Tech skills
Location requirements
Principal Software Engineer leading MLOps within Analytics Platform at Sun Life. Focused on AWS and machine learning operations, collaborating across technical and business teams.
Machine Learning Engineer designing and optimizing deep learning models for safety - critical environments at Destinus. Shaping the future of high - speed, autonomous flight technologies.
Machine Learning Engineer optimizing personalization systems for Spotify's audio streaming service. Collaborating with cross - functional teams to enhance user experience and deliver recommendations.
Principal Machine Learning Engineer developing ML and GenAI solutions in a cloud - native environment at Flexera. Leading a high - impact team and driving operational excellence for ML infrastructure.
Senior ML Platform/Ops Engineer building AI - powered ML pipelines for a dynamic Ed - Tech company. Collaborating with ML scientists and engineers to ensure reliable deployment and observability.
Senior ML Platform/Ops Engineer building ML systems for AI - powered learning at Preply. Productionizing machine learning with high reliability, performance, and observability in a hybrid environment.
Senior ML Platform/Ops Engineer at Preply building and maintaining ML pipelines, collaborating with ML Scientists and Data Engineers.
Machine Learning Engineer developing advanced Deep Learning models for autonomous driving technology at Mobileye. Collaborating in a high - end algorithmic engineering team on critical computer vision challenges.
Machine Learning Engineer focusing on vulnerabilities and security of AI systems at Carnegie Mellon University. Collaborating with a team to build robust prototypes and provide solutions for government sponsors.