MLOps Engineer

We are seeking an experienced MLOps Engineer to design, develop, and improve software that provides business, platform, and technology capabilities for our customers and colleagues.

Key Responsibilities

• Build and evolve model deployment pipelines, registries, and monitoring frameworks.
• Implement and optimise SageMaker, Databricks, MLflow, and related orchestration tools.
• Ensure compliance with model governance, auditability, and AI safety standards.
• Drive reproducibility, lineage tracking, drift detection, and rollback strategies.
• Write production-grade Python code for automation, orchestration, and monitoring.
• Partner with data scientists, quants, and infra teams to align MLOps capabilities with business needs.
• Coach engineers on best practices for ML lifecycle management and secure deployment.

Required Skills

• Strong Python engineering background.
• Deep experience with SageMaker, Databricks, MLflow, and CI/CD for ML.
• Proven track record in operationalizing ML models at scale.
• Knowledge of model governance, compliance, and risk management.
• Familiarity with feature stores, model registries, and observability tools.

Preferred Tech

• Kubernetes (EKS/on-prem), Airflow, Step Functions for orchestration.
• Databricks for data engineering and ML pipelines.
• MLflow for experiment tracking and model registry.
• SageMaker for training and deployment.
• Langfuse for evaluation and tracing.