Lead ML Ops Engineer (MLOps)

TL;DR

Lead ML Ops Engineer (MLOps): Defining and implementing standards for delivering, operating, and scaling machine learning systems in production with an accent on model serving, pipelines, and deployment. Focus on establishing robust production readiness criteria and evolving platform capabilities to reduce engineering effort.

Location: Hybrid, based in London

Company

hirify.global is a data-driven organization focused on machine learning systems.

What you will do

• Establish standard, reusable patterns for model serving, pipelines, and deployment.
• Define standards for model packaging, versioning, CI/CD, monitoring, and observability.
• Set clear production readiness criteria for models entering engineering workflows.
• Provide technical direction for ML solutions within the team.
• Evolve platform capabilities to reduce bespoke approaches and engineering effort required to productionise models.

Requirements

• Experience building and operating ML systems in production, including model serving, monitoring, and lifecycle management.
• Strong understanding of serving patterns across batch and real-time environments, and how models move from training to production.
• Experience defining engineering approaches that enable Data Science teams to deliver reliable, production-ready models.
• Ability to turn ambiguity into clear, adoptable technical standards and reusable patterns.
• Experience setting technical direction and influencing engineering practices across teams.
• Hands-on experience with cloud ML platforms (e.g., AWS SageMaker) and orchestration tools (e.g., Airflow, Step Functions).

Culture & Benefits

• You value structure, consistency, and high standards in engineering practices.
• You are motivated by improving reliability and long-term maintainability of ML systems.
• Committed to diversity and equal opportunity.
• Provide reasonable adjustments to support candidates during the application and interview process.
• Permanent position.