Machine Learning Operations Engineer

About

Are you passionate about taking machine learning models out of notebooks and into real‑world production systems? A fast‑growing fintech lender is looking for a Machine Learning Operations Engineer to bridge the gap between Data Science and Engineering. This role is perfect for someone who enjoys solving complex problems, building scalable ML infrastructure, and ensuring models deliver real business impact in production environments.

Responsibilities

• Model Deployment & MLOps

  • Productionising machine learning models developed by Data Scientists
  • Building and maintaining CI/CD pipelines for ML workflows
  • Creating reproducible environments for model training and inference
  • Implementing monitoring for data drift, model drift, and performance degradation

• Cloud Infrastructure

  • Designing and maintaining scalable AWS‑based ML infrastructure
  • Deploying ML solutions using AWS services such as SageMaker, Lambda, ECS/EKS, S3, and Step Functions
  • Building ETL/ELT pipelines for structured and nested data

• APIs & Integration

  • Developing APIs and microservices for model inference
  • Integrating ML services into real‑time lending decision engines
  • Ensuring low‑latency, fault‑tolerant services for production systems

• Collaboration & Ways of Working

  • Working closely with Data Scientists to understand models and features
  • Partnering with Engineering and Platform teams to integrate ML into production systems
  • Helping define and build MLOps standards and infrastructure as the business scales

Requirements

• Minimum 3+ years’ experience in Machine Learning Engineering, Data Engineering, or Software Engineering
• Strong Python development skills
• Proven experience deploying machine learning models into production environments
• Experience building CI/CD pipelines
• Experience working with AWS cloud infrastructure
• Strong SQL skills and data pipeline experience
• Experience building APIs or backend services

Nice‑to‑Have

• Experience in fintech, lending, or credit‑risk modelling
• Experience with SageMaker or similar ML deployment platforms
• Knowledge of feature engineering pipelines or model monitoring tools
• AWS certifications or formal MLOps training

Why Join the Team

• Work on real‑time machine learning systems that power automated lending decisions
• Play a key role in shaping MLOps infrastructure as the company scales
• Strong career growth opportunities within the Data Science and Engineering teams
• Hybrid‑friendly working environment
• Learning budget for AWS certifications and ML engineering development

Qualification

• Relevant degree in Computer Science, Engineering, Data Science, or a related technical field