Data Scientist - ML Engineering

TL;DR

Data Scientist - ML Engineering (MLOps): Architect end-to-end ML infrastructure across pipelines, serving, monitoring, and governance with an accent on deployment of high-impact models like forecasting engines, optimization solvers, and NLP models. Focus on designing advanced CI/CD workflows, implementing model registry and versioning, building monitoring for model drift, and mentoring MLOps engineers.

Location: Ciudad de Mexico

Company

hirify.global is a global AI-native technology solutions provider that develops AI-powered digital products and platforms, partnering with clients to leverage data and AI for business transformation.

What you will do

• Architect end-to-end ML infrastructure including pipelines, serving, monitoring, and governance.
• Lead deployment of high-impact models such as forecasting engines, optimization solvers, and NLP models.
• Design advanced CI/CD workflows using Azure Pipelines, MLflow, and Databricks.
• Implement model registry, versioning, lineage, and audit compliance.
• Build monitoring systems for model drift and retraining automation.
• Mentor MLOps engineers and guide cross-functional platform integration.
• Drive adoption of MLOps best practices from containerization to observability.

Requirements

• 5–8+ years in ML Engineering, MLOps, or high-scale ML systems.
• Deep expertise in Spark, Azure Databricks, MLflow, Kubernetes, and Docker.
• Proven track record deploying ML at enterprise scale with audit and monitoring layers.
• Familiarity with hybrid/multi-cloud infrastructure.

Nice to have

• AI Tooling Proficiency: Leverage AI tools for drafting, analysis, research, or process automation.
• Leadership experience in ML platform or DevOps teams.
• Experience with feature stores, feature engineering, AutoML, or H2O.

Culture & Benefits

• High-impact environment fostering growth, collaboration, and impact.
• Commitment to professional development.
• Flexible and collaborative culture with global opportunities.
• Vibrant community and total rewards (specific benefits determined by employment type and location).