Senior ML Engineer

Design, develop, and deploy ML models in AWS Sage Maker and EKS.

Optimize ML models for real-time decisioning in high-traffic 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 Control-M jobs.

Implement observability tools like Open Search, Fluent Bit, Prometheus, Kibana, Grafana, and AWS Cloud Watch.

Develop ETL/ELT pipelines for data preprocessing and feature engineering.

Work with AWS Redshift to process large-scale 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 on-call 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 (Tensor Flow, PyTorch, Scikit-learn).

Experience with AWS ML services (Amazon Sage Maker, EKS, Lambda, Redshift, Control-M, Terraform).

Experience with MLOps practices (CI/CD pipelines with Git Hub Actions, Docker, Kubernetes).

Proficiency in observability & monitoring tools:
Open Search, Fluent Bit, Kibana, Prometheus, Grafana, Cloud Watch.

Strong understanding of real-time 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.

Problem-Solving Ability to troubleshoot ML model failures in production.

Strong Communication Work effectively with cross-functional teams.

Agility Adapt to a fast-paced, high-stakes environment.