Senior ML Engineer, ML Platform - GFT
Core
Design and build a production-grade machine learning pipeline for financial risk model training and inference, supporting model lifecycle management from data preparation through operational inference.
Role type
Senior MLOps Engineer
Builds
Automated, auditable MLOps platform for model training, testing, registration, and deployment
Domain
Financial services / Machine Learning Operations
Deliverable
production ML models
Required skills
Python, PySpark, AWS (S3, EMR, Lambda, Step Functions, ECS/EKS, SageMaker, CloudWatch, IAM), CI/CD (GitHub Actions, Jenkins, CodePipeline), containerization, Linux, shell scripting, model lifecycle management, hybrid cloud/on-prem deployment
Preferred skills
Model monitoring and drift detection, distributed training frameworks (Ray, Spark, Dask), feature stores, data lineage systems, financial risk modeling workflows
Technologies
MLflow, SageMaker Model Registry, Airflow, AWS Step Functions, Stonebranch, Prefect, AWS EMR, Cloudera Data Platform
Responsibilities
Design and implement end-to-end reusable MLOps pipelines; Build and automate model lifecycle management workflows including versioning, promotion, approval, and deprecation; Develop and integrate a model registry to manage model metadata, lineage, and reproducibility; Orchestrate data and training workflows; Implement CI/CD pipelines; Build data preparation and training scripts optimized for performance; Manage model artifacts, dependencies, and environments; Ensure strong observability and auditability through structured logging and metrics; Collaborate with DevOps and data engineering teams for secure integration and production readiness
Seniority
Senior, hands-on IC
