Machine Learning Operations Engineer

SUMMARY:
Are you passionate about taking machine learning models out of notebooks and into real-world production systems? A fast-growing fintech lender, Our client 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.

POSITION INFO:

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
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

Contact Kayla Reddy on , quoting the Reference: CFA021179.