Remote AI Engineer - MLOps Specialist

Placements24; Gqeberha; Híbrido; ZAR 600 000 - 900 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

Health benefits

Annual leave

Paid sick leave

Learning budget

Remote-friendly culture

Team virtual events

Resumo da oferta

Placements24 is seeking a highly skilled Remote AI Engineer specializing in MLOps to design, deploy, and monitor scalable ML pipelines. You will build CI/CD for ML workloads, automate retraining, and ensure production reliability across AWS, Azure, or GCP, with Docker and Kubernetes orchestration.

The role requires a strong foundation in ML concepts, 4+ years in MLOps, and deep experience with Kubeflow, MLflow, and cloud tooling.

Qualificações

• Bachelor's degree in Computer Science, Engineering, or a related technical field.
• Proven experience (4+ years) in MLOps engineering or a similar role.
• Strong understanding of machine learning concepts and the ML lifecycle.
• Hands-on experience with MLOps tools and platforms (e.g., Kubeflow, MLflow, Seldon Core, BentoML).
• Proficiency in cloud platforms (AWS, Azure, or GCP) and their AI/ML services.
• Experience with containerization technologies (Docker) and orchestration tools (Kubernetes).
• Solid programming skills in Python and experience with CI/CD tools (e.g., Jenkins, GitLab CI, GitHub Actions).
• Familiarity with infrastructure as code (IaC) tools like Terraform or CloudFormation.
• Excellent problem-solving skills and the ability to work independently in a remote environment.
• Strong communication skills, with the ability to collaborate effectively across distributed teams.

Responsabilidades

• Design, implement, and manage scalable MLOps pipelines for model training, evaluation, deployment, and monitoring.
• Develop and maintain CI/CD strategies for machine learning models.
• Automate the process of model retraining and versioning to ensure models remain up-to-date and performant.
• Implement robust monitoring solutions to track model performance, data drift, and system health in production.
• Collaborate closely with AI/ML engineers and data scientists to deploy models efficiently and reliably.
• Containerize ML applications using Docker and orchestrate them using Kubernetes or similar platforms.
• Manage cloud infrastructure (AWS, Azure, or GCP) for AI/ML workloads.
• Troubleshoot and resolve issues related to ML deployments and infrastructure.
• Contribute to the development of best practices and documentation for MLOps.
• Stay current with the latest tools and trends in MLOps and AI infrastructure.

Conhecimentos

Python

ML concepts

Formação académica

Bachelor's degree in Computer Science or related field

Ferramentas

Docker

Kubernetes

Kubeflow

MLflow

Seldon Core

BentoML

Jenkins

GitLab CI

GitHub Actions

Terraform

AWS

Azure

GCP

Descrição da oferta de emprego

About the Role

Our client, a dynamic and rapidly growing firm in the AI & Emerging Technologies space, is seeking a highly skilled Remote AI Engineer with a specialization in MLOps. This is a fully remote position, offering the flexibility to work from anywhere. You will be instrumental in building, deploying, and maintaining robust machine learning pipelines and infrastructure. Your primary focus will be on ensuring the seamless operation, scalability, and reliability of our AI models in production environments. This role requires a deep understanding of both AI/ML concepts and DevOps practices, enabling you to bridge the gap between development and operations. If you are passionate about automating and streamlining the ML lifecycle and thrive in a remote-first setting, this is the perfect opportunity for you.

Key Responsibilities

Design, implement, and manage scalable MLOps pipelines for model training, evaluation, deployment, and monitoring. Develop and maintain CI/CD (Continuous Integration/Continuous Deployment) strategies for machine learning models. Automate the process of model retraining and versioning to ensure models remain up-to-date and performant. Implement robust monitoring solutions to track model performance, data drift, and system health in production. Collaborate closely with AI/ML engineers and data scientists to deploy models efficiently and reliably. Containerize ML applications using Docker and orchestrate them using Kubernetes or similar platforms. Manage cloud infrastructure (AWS, Azure, or GCP) for AI/ML workloads. Troubleshoot and resolve issues related to ML deployments and infrastructure. Contribute to the development of best practices and documentation for MLOps. Stay current with the latest tools and trends in MLOps and AI infrastructure.

Requirements

Bachelor's degree in Computer Science, Engineering, or a related technical field. Proven experience (4+ years) in MLOps engineering or a similar role focusing on deploying and managing ML systems. Strong understanding of machine learning concepts and the ML lifecycle. Hands-on experience with MLOps tools and platforms (e.g., Kubeflow, MLflow, Seldon Core, BentoML). Proficiency in cloud platforms (AWS, Azure, or GCP) and their AI/ML services. Experience with containerization technologies (Docker) and orchestration tools (Kubernetes). Solid programming skills in Python and experience with CI/CD tools (e.g., Jenkins, GitLab CI, GitHub Actions). Familiarity with infrastructure as code (IaC) tools like Terraform or CloudFormation. Excellent problem-solving skills and the ability to work independently in a remote environment. Strong communication skills, with the ability to collaborate effectively across distributed teams.

Benefits

Our client offers a competitive salary package, commensurate with experience, and performance-based bonuses. As a fully remote position, we provide flexibility in your work location. Employees receive comprehensive health benefits, including medical and dental coverage. We offer a generous annual leave policy and paid sick leave. Access to online learning platforms, virtual training sessions, and conference attendance budgets are provided for continuous professional development. Our client fosters a collaborative and inclusive remote work culture, with regular virtual team events and communication channels designed to keep everyone connected and engaged, irrespective of their geographical location.