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Senior ML Engineer 12 Month Contract
Design develop and deploy ML models in AWS SageMaker and EKS.
Optimize ML models for realtime decisioning in hightraffic environments.
Ensure models comply with regulatory and security standards.
Build and maintain CI/CD pipelines for ML model deployments.
Automate model retraining monitoring and logging using AWS Lambda Terraform and ControlM jobs.
Implement observability tools like OpenSearch FluentBit Prometheus Kibana Grafana and AWS CloudWatch.
Develop ETL/ELT pipelines for data preprocessing and feature engineering.
Work with AWS Redshift to process largescale datasets for model training.
Monitor ML models running 24/7 in production ensuring reliability and high availability.
Work closely with engineering teams to troubleshoot and optimize production systems.
Participate in an oncall rotation for urgent ML pipeline issues.
Collaborate with data scientists decision engineers and credit engineers to align ML solutions with business needs.
Take ownership of ML solutions and provide guidance to junior engineers.
Contribute to the ongoing AI/ML strategy within the business.
5 years of experience in Machine Learning Engineering.
Strong expertise in Python PySpark SQL and ML libraries (TensorFlow PyTorch Scikitlearn).
Experience with AWS ML services (Amazon SageMaker EKS Lambda Redshift ControlM Terraform).
Experience with MLOps practices (CI/CD pipelines with GitHub Actions Docker Kubernetes).
Proficiency in observability & monitoring tools: OpenSearch FluentBit Kibana Prometheus Grafana CloudWatch.
Strong understanding of realtime ML applications in financial environments.
Experience in building and maintaining ETL pipelines in a cloud environment.
Leadership & Ownership Ability to work independently and drive ML initiatives.
ProblemSolving Ability to troubleshoot ML model failures in production.
Strong Communication Work effectively with crossfunctional teams.
Agility Adapt to a fastpaced highstakes environment.
Banking Industry Experience Preferred.
Full Time