Machine Learning Engineer

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Enfuce

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2016

🏦 Banking

💳 Fintech

Banking • Fintech • Digital banking

Enfuce is a leading provider of secure, scalable, and compliant card issuing and payment processing solutions. Their cloud-based platform offers cutting-edge features such as advanced spend controls, digital wallets, fraud and dispute management, and 3D secure authentication with an exceptional uptime. Enfuce supports multi-currency and multi-country programs, making it easy for businesses to scale globally. As principal members of Visa and Mastercard, they ensure seamless and fast deployment of card services. Enfuce works with various industries, providing tailored solutions for banking, fintech, alternative lending, and more. Their commitment to innovation and customer-centric approach has made them a preferred partner for digital banking solutions.

📋 Description

• Design, build, and maintain scalable MLOps infrastructure for machine learning and Generative AI applications. • Develop automated training, validation, testing, deployment, and CI/CD pipelines for machine learning models. • Implement experiment tracking, model versioning, model registries, and artifact management using MLOps best practices. • Build and maintain workflow orchestration, feature engineering, and data processing pipelines. • Monitor production ML systems, including model performance, data quality, drift detection, latency, and overall system health. • Manage the end-to-end model lifecycle, including retraining, rollback, reproducibility, governance, and auditability. • Containerize ML workloads with Docker and deploy scalable services using cloud-native technologies and orchestration platforms. • Develop and maintain Infrastructure as Code (IaC) for AI platforms and cloud resources. • Collaborate with Data Scientists and software engineers to productionize, optimize, and scale machine learning solutions. • Evaluate and implement new MLOps tools, frameworks, and best practices, including support for LLM and agentic AI applications.

🎯 Requirements

• Bachelor's or Master's degree in Computer Science, Machine Learning, Software Engineering, or a related field. • Strong Python programming skills and proficiency with SQL. • Experience with MLflow for experiment tracking, model registry, versioning, and model lifecycle management. • Experience with modern ML platforms such as Snowflake, dbt, Snowpark ML, Vertex AI, or Amazon SageMaker. • Strong understanding of the end-to-end machine learning lifecycle, including experimentation, deployment, monitoring, retraining, and governance. • Experience with Git, software engineering best practices, and Infrastructure as Code (e.g., Terraform or CloudFormation). • Experience with Docker, containerized ML workloads, and container orchestration platforms such as Kubernetes. • Hands-on experience with cloud platforms such as AWS, Azure, or Google Cloud Platform, including production monitoring and observability. • Familiarity with feature stores, model registries, artifact repositories, and modern MLOps practices. • Experience deploying LLM or Generative AI applications is a strong advantage, along with excellent problem-solving, communication, and collaboration skills.

🏖️ Benefits

• Fair pay and employee stock option: We value the input of every employee and want you to tap into the growth we build together. That’s why our salaries are competitive and reassessed regularly, and you have access to an employee stock option program. • Flexible Paid Time Off: We offer a flexible paid time off policy, providing up to 5 weeks of annual vacation days and paid family leave (subject to country regulations). Additionally, you can benefit from hybrid or remote work options, promoting a healthy work-life balance. • Regular Fun With Your Team: To spend other than work-related time with your teammates, you get a team activity budget for three quarters a year. The fourth quarter is reserved for a company-wide event.