MLOps Engineer

About This Job Opportunity
Our Vision
To be the world's most trusted global payroll partner, simplifying pay for all employees.

Our Mission
Empowering global workforces with seamless, compliant, and innovative payroll and payment solutions, enabling businesses to thrive in a connected world.

Our People
Our fundamental beliefs at CloudPay are built on core values of professionalism, passion, empowerment, innovation, and teamwork. We value our employees and strive to create a great workplace where everyone is valued, heard, inspired, and encouraged to bring their authentic selves to work. We're committed to providing an excellent employee experience through fulfilling projects, empowerment to make a difference, and an environment that inspires innovation.

What Makes This Role Exciting
We're looking for a hands-on and forward-thinking MLOps Engineer to join our growing Data team. You'll bridge the gap between Data Science and Engineering, building scalable, automated, and secure ML infrastructure that enables the delivery of AI solutions across CloudPay's global payroll and payments platforms.

You'll be instrumental in designing and maintaining the pipelines, tools, and frameworks that power model training, deployment, and monitoring, ensuring our AI products operate reliably and efficiently at scale.

If you're passionate about automation, scalability, and bringing AI models to life in production, this is the role for you.

Main Responsibilities
ML Infrastructure & Automation

• Design, build, and maintain end-to-end MLOps pipelines for model training, validation, deployment, and monitoring in production environments.
• Develop CI/CD workflows and automate retraining, testing, and rollback processes for ML models.
• Collaborate with Data Scientists and Data Engineers to transform prototypes into robust, production-grade systems.

Deployment & Monitoring

• Manage model serving using AWS-native tools (SageMaker, Fargate, ECS, Bedrock) and integrate with the broader CloudPay data ecosystem (Snowflake, dbt, Prefect).
• Implement model observability tools and frameworks for performance tracking, drift detection, and lineage management.
• Establish and maintain environments for model experimentation, testing, and reproducibility.

Collaboration & Enablement

• Partner with Data Engineering to align model pipelines with enterprise data architecture and ensure efficient data ingestion and preprocessing.
• Work closely with Platform and DevOps teams to align ML deployment standards with overall infrastructure and security best practices.
• Contribute to ML governance frameworks, including metadata management, reproducibility, and model registry.

Innovation & Continuous Improvement

• Evaluate new MLOps tools, frameworks, and AI infrastructure technologies to improve scalability, cost efficiency, and automation.
• Advocate for engineering excellence, coding standards, and documentation across the ML lifecycle.
• Support the evolution of CloudPay's AI ecosystem, enabling GenAI and LLM-based solutions to move seamlessly from research to production.

Experience Needed For This Role

• Degree in Computer Science, Data Engineering, AI/ML, or a related technical field.
• Relevant cloud or MLOps certifications (AWS Certified Machine Learning, Kubernetes, etc.) are a plus.
• Solid experience in MLOps, Data Engineering, or ML Engineering roles, preferably in cloud-based environments.
• Proven experience deploying and managing ML models in production, including monitoring and performance optimization.
• Background in supporting AI/ML use cases (OCR, NLP, document understanding, recommendations, or similar).

Technical Skills

• Strong proficiency in Python, with experience in frameworks such as FastAPI, MLflow, Airflow, or Prefect.
• Expertise in cloud platforms (AWS preferred).
• Familiarity with containerization and orchestration (Docker, Kubernetes, ECS).
• Experience with model registry, experiment tracking, and CI/CD tools (e.g., GitHub Actions, Jenkins, or similar).
• Solid understanding of data engineering principles, feature stores, and versioning for ML data.
• Experience with monitoring tools for ML (EvidentlyAI, Prometheus, Grafana, or similar).

Leadership & Communication

• Ability to translate complex technical solutions into clear business impact.
• Strong cross-functional collaboration skills with Data Science, DevOps, and Product teams.
• o Detail-oriented, self-driven, and comfortable working in a fast-paced, global environment.
• Excellent written and oral communication skills in English

About You And Our Core Values

• Taking ownership, working with integrity and respect
• Being a team player is key to our culture
• Solution and customer focused
• Great initiative with the goal for excellence in achieving results
• Dedicated to developing and always looking for continuous improvements
• Be creative, be committed, be engaged and enjoy what you do

CloudPay is committed to being an equal opportunities employer. Competitive benefits for Spain, Poland and the Philippines.
The CloudPay culture is built upon on five core values, from which we develop our service, our technology and our business strategies. Our fundamental beliefs are a promise to our employees, customers and partners, built on the core values of professionalism, passion, empowerment, innovation, and teamwork.

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