Machine Learning Engineer (Model Deployment / MLOps) (CPT Hybrid / Remote)

ENVIRONMENT

A fast‑paced FinTech company seeks a passionate Machine Learning Engineer (MLOps focus) to power instant lending decisions—no humans in the loop. Its models drive credit risk, portfolio management and lifecycle decisioning with the biggest challenge being moving models from Data Science into reliable production systems. They’re looking for you to bridge that gap and ensure that every model built makes it into production—fast reliable and cost‑efficient. The ideal candidate will require a Postgraduate Degree in a numerate discipline such as Statistics / Mathematics / Software Engineering or a related field with 3+ years’ experience in ML Engineering, Data Engineering or Software Engineering with focus on ML deployment. You also need a proven track record of deploying ML models into production (SageMaker, Lambda, Step Functions or equivalent), strong SQL, PostgreSQL, Node.js, Python or JavaScript and AWS infrastructure (EC2, ECS / EKS, S3, Lambda, Glue, Step Functions).

DUTIES

Model Deployment & MLOps

• Take models from Data Scientists (notebooks, prototypes) and productionize them into scalable APIs and pipelines.
• Build CI / CD pipelines for ML : automated testing, validation, deployment, rollback.
• Implement monitoring for data drift, model drift and performance decay with automated alerts and retraining triggers.
• Maintain reproducible environments for training and inference (Docker, SageMaker, Lambda, Step Functions).

Infrastructure & Pipelines

Integration & APIs

Collaboration

REQUIREMENTS

Qualifications

MUST-HAVEs

Nice‑to‑have skills

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