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Open nowFirst seen 3 days ago

Senior ML Engineer

Boardroom Appointments3,354 open roles

Where
Cape Town, Western Cape, South Africa
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Your applicationOpen nowSenior ML EngineerBoardroom Appointments · Cape Town, Western Cape, South Africa

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  2. CV RocketCV written for this posting.

  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

  4. CV RocketApplied on Boardroom Appointments's own form.

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The clock on this job

Early applications get read.

12.1% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.1%7 days
  4. 19.1%14 days
  5. 35.6%30 days
  6. 52.7%60 days
This job: first seen 3 days ago

The posting

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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