Jeeny

Tech / AI / Software

MLSystemsEngineer

karachi, sindh, pakistan FULL TIME
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Mid candidates.

The Brief

“ML Systems Engineer at Jeeny. Skills: ML Systems Engineer, MLOps, backend engineering, production ML systems. Design, build, and maintain production-ready systems for deploying machine learning models. Develop and manage APIs and backend services that power ML-driven features”

Industry & Context.

Tech / AI / Software
Problems you'll solve

problem-solving skills

What They're Looking For.

Must Have

Bachelor’s degree in Computer Science, Software Engineering, Data Engineering, or a related field, 3+ years of experience in backend engineering, ML systems, or MLOps, experience with Python and backend frameworks, Experience building and maintaining APIs and microservices, Familiarity with ML deployment workflows and model serving frameworks, Experience with data pipelines and workflow orchestration tools, understanding of cloud platforms, Experience with containerization and orchestration tools (Docker, Kubernetes), Knowledge of monitoring and observability tools for production systems, problem-solving skills and ability to work cross-functionally

Nice to Have

Kubernetes a plus

What You'll Do.

and maintain production-ready systems for deploying machine learning models

Develop and manage APIs and backend services that power ML-driven features

Build and maintain feature pipelines to support model training and inference workflows

Create monitoring dashboards and observability tools to track model performance and system health

Automate ML deployment workflows and operational processes to improve reliability and efficiency

Improve system scalability

and maintainability of ML-powered applications

Troubleshoot and resolve issues related to production ML systems and deployment pipelines

Implement best practices for versioning

and maintaining ML services

How You'll Work.

Team & Collaboration

Collaborate closely with Data Science, QA, and DevOps teams to ensure smooth model integration and deployment

Full Job Description

**About Jeeny: ** Jeeny is a leading ride-hailing platform that strives to revolutionize daily commuting and transportation. Our app connects users with their preferred modes of transportation, making mobility accessible, convenient, and affordable for all. We are a joint venture between MEIG (Middle East Internet Group), Rocket Internet, and IMENA. Since our inception, we have grown exponentially and currently operate in Saudi Arabia and Jordan. At Jeeny, we value innovation, teamwork, and a passion for delivering exceptional user experiences. Join us in our mission to transform the transportation landscape. ** About the Role: ** We are looking for an **ML Systems Engineer** to build the services, interfaces, and automation required to reliably operate ML-powered features in production. This role is focused on transforming data science models into complete production systems, including APIs, feature pipelines, monitoring dashboards, and operational tooling. The ideal candidate will serve as the bridge between **Data Science, QA, and DevOps** , ensuring machine learning models are not only accurate, but also deployable, observable, scalable, and maintainable. ** Responsibilities:** * Design, build, and maintain production-ready systems for deploying machine learning models. * Develop and manage APIs and backend services that power ML-driven features. * Build and maintain feature pipelines to support model training and inference workflows. * Create monitoring dashboards and observability tools to track model performance and system health. * Automate ML deployment workflows and operational processes to improve reliability and efficiency. * Collaborate closely with **Data Science, QA, and DevOps** teams to ensure smooth model integration and deployment. * Improve system scalability, performance, and maintainability of ML-powered applications. * Troubleshoot and resolve issues related to production ML systems and deployment pipelines. * Implement best practices for vers

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