Lead ML Engineer (MLOps)

TL;DR

Lead ML Engineer (MLOps): Establishing standards, patterns, and technical foundations for delivering and operating machine learning models in production with an accent on MLOps lifecycle management, model serving, and CI/CD pipelines. Focus on reducing engineering effort through reusable platform capabilities and defining production readiness criteria for data-driven systems.

Location: London (Hybrid)

Company

hirify.global is a data-driven financial services organization focused on delivering robust and scalable machine learning solutions.

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 delivered within the team.
• Evolve platform capabilities to reduce bespoke approaches over time.
• Reduce engineering effort required to productionise models by establishing reusable approaches.

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.
• Experience defining engineering approaches that enable Data Science teams to deliver production-ready models.
• Hands-on experience with cloud ML platforms such as AWS SageMaker and orchestration tools like Airflow or Step Functions.
• Ability to turn ambiguity into clear, adoptable technical standards and reusable patterns.
• Strong communication skills and ability to collaborate across Data Science, Engineering, and Data Platforms.

Culture & Benefits

• Inclusive and diverse working culture that values authenticity.
• Commitment to reasonable adjustments for candidates with disabilities or caring responsibilities.
• Focus on long-term maintainability and engineering excellence.
• Collaborative environment working across multiple technical domains.