Own the end-to-end operationalization of ML and AI solutions—from development to scalable, reliable production systemsthat integrate seamlessly with other digital tools.
Design, automate, and maintain CI/CD pipelines for model training, testing, deployment, and retraining (Azure DevOps, Databricks).
Build, optimize, and version model lifecycle workflows, ensuring reproducibility, lineage, and governance across the ML/AI platform.
Monitor production models for performance, drift, reliability, and resource usage; implement automated retraining workflows.
Optimize compute, storage, and orchestration across the Databricks platform to ensure efficient, cost-effective operations.
Collaborate closely with ML/AI Scientists, Data Engineers, and DWH team to transform research-grade models into production-ready services.
Contribute to advancing our ML/AI platform, tooling, automation standards, and best practices.

Solid experience in operationalizing ML/AI models, including deployment, automation, monitoring, and lifecycle management.
Strong programming skills in Python, PySpark, and SQL with clean, efficient, production-ready code.
Experienced in feature engineering with a practical understanding of data engineering fundamentals
designing, validating, and optimizing feature pipelines, and ensuring feature consistency
Experience in building Vector embeddings & RAG systems.
Familiarity in ML and LLM models development and libraries used.
Experience with MLflow (or similar tools) for model tracking, registry management, and lifecycle operations.
Familiarity with CI/CD pipelines (Azure DevOps preferred)
Strong grasp of data versioning, model versioning, reproducibility, and data lineage within governed ML/AI environments.
Experience designing, consuming, or integrating REST APIs to expose ML/AI models as services and support real-time or near-real-time inference.
Experience monitoring production models, identifying drift or performance issues, and implementing corrective workflows.
A collaborative, systems-thinking mindset, working closely with ML/AI Scientists, Data Engineers, and Data Warehouse team.

Two weeks of paid vacation, 12 statutory holidays, plus 4 extra global VeeaMe Days for self-care and 24 paid volunteer hours annually through Veeam Cares
Paid parental leave: 8 days for fathers, 122 days for birthing parents, 92 days for adoptive parents
Medical, dental, and vision coverage fully funded through INS Premium for employees and dependents
Mental health support, therapy sessions, and virtual care via our Employee Assistance Program
Retirement and social security contributions through Costa Rica’s statutory programs
Life insurance equal to 24x monthly salary, plus disability and funeral coverage
Daily cafeteria subsidy
Fertility, adoption, and surrogacy support, plus 24 paid volunteer hours through Veeam Cares
Opportunities to learn and grow through on-demand libraries (LinkedIn Learning, O’Reilly), mentoring, workshops, and learning events like our annual Global Day of Learning

ML/AI Ops Engineer

Key skills