Machine Learning Operations Specialist | AI Platform Engineer

T.CHOITHRAM AND SONS (L.L.C); Abu Dhabi; On-site; AED 180,000 - 300,000; Full time

Benefits offered by this job

Competitive salary

Career development

Cloud & Kubernetes exposure

GPU technologies access

Job summary

T.CHOITHRAM AND SONS (L.L.C) in the United Arab Emirates is seeking a Machine Learning Operations Specialist / AI Platform Engineer to design, build, and optimize scalable ML infrastructure across cloud, on‑prem, and hybrid environments.

You will collaborate with data scientists, ML engineers, DevOps, and security teams to automate model deployment, ensure reliability, and manage lifecycle from development to production.

Qualifications

• Bachelor’s degree in Computer Science, AI, ML, or related field.

Responsibilities

• Design, build, and maintain scalable infrastructure for ML/AI workloads.

Skills

CI/CD

Git

Automation

DevOps

Troubleshooting

Documentation

Communication

Cross-functional collaboration

Multi-project management

Problem solving

Analytical thinking

Security awareness

Education

Bachelor’s degree in Computer Science, Artificial Intelligence, Machine Learning, Software Engineering, Data Engineering, or a related field

Tools

MLflow

Kubeflow

Airflow

SageMaker

Vertex AI

Azure Machine Learning

AWS

Google Cloud

Docker

Kubernetes

Helm

Terraform

CloudFormation

Pulumi

Job description

We’re Hiring: Machine Learning Operations Specialist | AI Platform Engineer

Employment Type: Full-Time

Level: Mid-Level to Senior

We are seeking a highly skilled Machine Learning Operations Specialist | AI Platform Engineer to build, operate, and optimize scalable platforms for machine learning and artificial intelligence workloads. This role will work closely with Data Scientists, ML Engineers, Software Engineers, DevOps, Cloud, and Security teams to establish reliable ML infrastructure, automate model deployment, and support the full machine learning lifecycle from development to production.

Key Responsibilities

• Design, build, and maintain scalable infrastructure for machine learning and AI workloads.
• Develop and operate ML platforms supporting model development, training, deployment, monitoring, and lifecycle management.
• Build automated MLOps pipelines for model training, validation, deployment, and retraining.
• Implement CI/CD and continuous integration practices for machine learning models and applications.
• Manage model versioning, artifact management, experiment tracking, and reproducibility.
• Automate machine learning workflows using appropriate orchestration and pipeline technologies.
• Deploy machine learning models into production environments across cloud, on-premises, or hybrid infrastructure.
• Build scalable model-serving infrastructure capable of supporting high-volume inference workloads.
• Monitor model performance, system health, resource utilization, latency, availability, and reliability.
• Establish monitoring for model drift, data drift, prediction quality, and production performance.
• Develop automated alerts and remediation processes for model and infrastructure issues.
• Collaborate with Data Scientists to productionize experimental models and machine learning solutions.
• Work with ML Engineers to improve model deployment, scalability, reliability, and operational efficiency.
• Support GPU and high-performance computing infrastructure for AI and machine learning workloads.
• Optimize compute, storage, networking, and cloud resources used by AI platforms.
• Implement Infrastructure as Code to automate the provisioning and management of AI infrastructure.
• Manage containerized machine learning environments using Docker and Kubernetes.
• Build reusable platform components and self-service tools for data science and ML teams.
• Establish standardized environments for model development, testing, staging, and production.
• Implement secure access controls, secrets management, encryption, and infrastructure security practices.
• Support data and model governance requirements throughout the machine learning lifecycle.
• Maintain model deployment standards, operational procedures, runbooks, and technical documentation.
• Troubleshoot model-serving, infrastructure, pipeline, deployment, and performance issues.
• Conduct root-cause analysis and implement long-term solutions for recurring platform problems.
• Support cloud migration, AI platform modernization, and large-scale machine learning initiatives.
• Evaluate new MLOps tools, AI infrastructure technologies, model-serving platforms, and automation frameworks.
• Establish platform reliability and operational metrics for ML and AI services.
• Support disaster recovery, backup, high availability, and business continuity for critical AI platforms.
• Collaborate with cybersecurity teams to ensure AI infrastructure meets organizational security requirements.
• Continuously improve automation, scalability, observability, reliability, and developer experience across the AI platform.

Requirements

• Bachelor’s degree in Computer Science, Artificial Intelligence, Machine Learning, Software Engineering, Data Engineering, or a related field.
• Previous experience in MLOps, Machine Learning Engineering, AI Platform Engineering, DevOps, Cloud Engineering, or a similar role.
• Strong understanding of machine learning lifecycle management and production ML systems.
• Hands‑on experience deploying and operating machine learning models in production.
• Experience with MLOps platforms and tools such as MLflow, Kubeflow, Airflow, SageMaker, Vertex AI, Azure Machine Learning, or equivalent technologies.
• Strong experience with cloud platforms such as AWS, Microsoft Azure, or Google Cloud.
• Experience with Docker, Kubernetes, Helm, and containerized workloads.
• Strong understanding of CI/CD, Git, automation, and DevOps practices.
• Experience implementing Infrastructure as Code using Terraform, CloudFormation, Pulumi, or similar technologies.
• Strong programming or scripting skills using Python, Bash, Go, or similar languages.
• Understanding of machine learning frameworks such as PyTorch, TensorFlow, Scikit‑learn, or equivalent technologies.
• Experience with model serving, APIs, inference infrastructure, and production ML systems.
• Knowledge of model monitoring, data drift, model drift, performance monitoring, and observability.
• Understanding of distributed systems, cloud architecture, networking, storage, and compute infrastructure.
• Experience working with GPU infrastructure or accelerated computing is advantageous.
• Familiarity with data pipelines, feature stores, model registries, and experiment‑tracking systems.
• Understanding of AI security, access control, secrets management, and data‑protection practices.
• Strong troubleshooting, debugging, and root‑cause‑analysis skills.
• Experience with monitoring and observability tools such as Prometheus, Grafana, Datadog, or similar platforms.
• Strong understanding of scalable, highly available, and fault‑tolerant infrastructure.
• Excellent technical documentation and communication skills.
• Ability to collaborate effectively with Data Scientists, ML Engineers, Software Engineers, DevOps, Security, and business teams.
• Strong problem‑solving and analytical capabilities.
• Ability to manage multiple technical projects and priorities in a fast‑paced environment.
• Relevant cloud, Kubernetes, AI, ML, or MLOps certifications are advantageous.

What We Offer

• Competitive salary and comprehensive employee benefits.
• Opportunity to build and operate modern AI and machine learning platforms.
• Exposure to MLOps, cloud computing, Kubernetes, automation, and AI infrastructure.
• Professional development in machine learning operations, AI engineering, cloud architecture, and DevOps.
• Access to modern AI platforms, cloud infrastructure, GPU technologies, and MLOps tools.
• Opportunities to lead production AI and machine learning platform initiatives.
• Exposure to advanced machine learning and artificial intelligence projects.
• Support for relevant cloud, AI, Kubernetes, and professional certifications.
• Collaborative engineering environment focused on automation, reliability, scalability, and innovation.
• Recognition for improving ML platform performance, reliability, and operational efficiency.
• Career progression toward Senior MLOps Specialist, AI Platform Engineer, MLOps Manager, AI Infrastructure Lead, or Head of AI Platform Engineering.