Senior ML Engineer

3 days ago

Cape Town, Western Cape, South Africa Boardroom Appointments Full time R800 000 - R1 200 000 per year


Senior ML Engineer - 12 Month Contract

Key Responsibilities

  • Design, develop, and deploy ML models in AWS SageMaker 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 OpenSearch, FluentBit, Prometheus, Kibana, Grafana, and AWS CloudWatch.

  • 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.

Required Skills & Qualifications:
Technical Skills:

  • 5+ years of experience in Machine Learning Engineering.

  • Strong expertise in Python, PySpark, SQL, and ML libraries (TensorFlow, PyTorch, Scikit-learn).

  • Experience with AWS ML services (Amazon SageMaker, EKS, Lambda, Redshift, Control-M, 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 real-time ML applications in financial environments.

  • Experience in building and maintaining ETL pipelines in a cloud environment.

Soft Skills:

  • 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.

  • Banking Industry Experience Preferred.


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