ML Ops Engineer

Overview

We are seeking a skilled ML Ops Engineer to join Miral Destinations' AI team in Abu Dhabi. This role focuses on maintaining and enhancing the infrastructure and pipelines that support the deployment, monitoring, and optimization of AI models in production environments. The successful candidate will ensure reliability and scalability of AI services, working closely with data scientists and other engineering teams.

Key Responsibilities

• Develop and sustain machine learning infrastructure and CI/CD workflows supporting Miral Destinations' AI platforms.
• Deploy machine learning models created by the AI Data Scientists into robust, scalable production environments.
• Continuously monitor model and system performance, including data drift and pipeline health, promptly addressing operational challenges.
• Enhance infrastructure with a focus on optimizing costs, reducing latency, and improving scalability across workloads.
• Provide ongoing operational maintenance and incident management for production AI systems.
• Automate workflows for model retraining, versioning, and release management utilizing platforms such as Databricks and MLflow.
• Integrate enterprise platforms and adhere to organizational architectural standards for AI and data systems.
• Uphold security, governance, and compliance criteria across all AI operational activities.
• Collaborate effectively with AI, Data Engineering, Business Intelligence, and Enterprise Data teams to utilize shared resources and best practices.
• Coordinate with enterprise platform and data engineering to standardize deployment, monitoring, and operations across the organization.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Software or Data Engineering, Artificial Intelligence, or a related discipline.
• 3 to 5 years' experience in ML Ops, DevOps, DataOps, or ML/data engineering roles.
• Proven track record deploying and managing ML models in production environments.
• Strong skills in troubleshooting data pipelines and creating simple pipelines as necessary.
• Solid understanding and application of MLOps best practices for scalable AI deployment.
• Intermediate data engineering abilities, including pipeline debugging and basic pipeline construction.
• Hands-on experience programming in Python, preferably with exposure to Databricks and MLflow platforms.
• Familiarity with CI/CD processes, container technologies such as Docker and Kubernetes, and infrastructure-as-code methodologies.
• Expertise in model monitoring including observability, drift detection, and data pipeline supervision.
• Experience working with leading cloud platforms like AWS, Azure, or Google Cloud Platform (GCP).
• Capability to optimize infrastructure focusing on cost efficiency, latency reduction, and scalability enhancements.

Desirable Skills

• Practical exposure to Databricks and enterprise-scale data platforms.
• Experience deploying retrieval-augmented generation (RAG) and large language model (LLM) solutions in production environments.