Main job function
Our client provides highly specialised solutions to large and small enterprises in both the private and public sectors. Our team of experts leverages cutting-edge technologies to address complex business challenges and drive digital transformation.
We are seeking a highly experienced AWS MLOps Engineer with a minimum of 3 years of AWS experience and at least 5 years in Machine Learning. The ideal candidate will play a crucial role in designing, implementing, and maintaining machine learning (ML) solutions on the AWS platform. As an AWS MLOps Engineer, you will work closely with cross-functional teams to deploy and operationalize ML models, ensuring seamless integration into our clients’ environments.
Job family: Computer Science/Engineering/Mathematics/Statistics
5 years of experience in MLOps or related roles 3 years of experience with AWS
Bachelor’s or higher degree in Computer Science, Engineering, Statistics, or a related field. Advantageous:
- AWS Certified Machine Learning – Specialty Certificate
- AWS Certified DevOps Engineer – Professional
- Other relevant AWS certifications (e.g., Solutions Architect, Developer
Responsibilities differ across client engagements but may include:
- Architect and Deploy ML Solutions: Design and implement end-to-end ML pipelines on the AWS platform, ensuring scalability, reliability, and compliance with banking sector
- Model Training and Evaluation: Collaborate with data scientists to facilitate the training and evaluation of ML models, optimizing for accuracy and efficiency.
- Infrastructure Management: Build and manage infrastructure on AWS, including EC2 instances, S3 buckets, and other relevant services, adhering to the highest security standards required by the banking sector.
- Automation and Orchestration: Implement automation and orchestration tools to streamline ML workflows and enhance operational efficiency.
- Monitoring and Logging: Develop robust monitoring and logging mechanisms to track the performance of ML models in production, identifying and addressing issues
- Security and Compliance: Implement security best practices and ensure compliance with data protection regulations in ML workflows and infrastructure.
- Collaboration and Documentation: Work closely with cross-functional teams, providing technical expertise and documentation to support knowledge transfer.
Qualifications and Competencies
- AWS Expertise: In-depth knowledge of AWS services, with a focus on those relevant to ML, such as SageMaker, Lambda, Glue, and others.
- MLOps Proficiency: Hands-on experience with MLOps practices, including model versioning, continuous integration, and continuous deployment (CI/CD) for ML
- Programming Skills: Proficient in programming languages such as Python or C#, with the ability to write clean and efficient code for ML workflows.
- Containerization and Orchestration: Experience with containerization tools (Docker) and orchestration frameworks (Kubernetes) for deploying and managing ML
- Infrastructure as Code (IaC): Familiarity with IaC tools like Terraform or AWS CloudFormation for automating infrastructure deployment.
- Monitoring and Logging Tools: Knowledge of monitoring and logging tools such as CloudWatch, ELK stack, or Prometheus for tracking ML model performance.
- Collaboration and Communication: Strong collaboration and communication skills to work effectively with cross-functional teams and clients.
R500.00 to R700.00 per hour.
A Consultant will be in touch if you are shortlisted for the position. Please consider your application unsuccessful should you not have been contacted within 2 weeks. We will keep your CV on our database and contact you should you match the criteria of any other vacancies.
To apply for this job email your details to Sharonsmit@armstrongappointments.com