Senior MLOps Engineer

Headquartered in Abu Dhabi, United Arab Emirates, is an enterprise technology company specializing in AI infrastructure. It builds platforms that integrate intelligence into core operations, delivering real-time recommendations, automation, and human‑like interactions through robust MLOps systems. The company focuses on scaling secure, reliable AI solutions for mission‑critical environments, combining deep technical expertise with practical engineering to close the gap between model development and production.

As a Senior MLOps Engineer at AI71, you will play a pivotal role in shaping the infrastructure that powers our AI‑enabled enterprise platform-an early‑stage system designed to automate complex workflows, deliver real‑time insights, and enable data‑driven decision‑making ike traditional AI solutions, our platform embeds intelligence at its core, driving predictive recommendations, intelligent automation, and natural‑language interfaces that transform how users interact with complex systems.

This role is focused on building the foundational layers that ensure models are deployed, monitored, and maintained reliably for enterprise customers. You will design and construct the model serving infrastructure - including real‑time inference, batch processing, and LLM endpoints - while establishing the CI/CD pipelines, observability frameworks, and evaluation systems required to support mission‑critical AI workloads. Your work will bridge the gap between model development and production, ensuring seamless transitions from notebook experiments to scalable, secure, and cost‑efficient deployments.

Given the early‑stage nature of the platform, you will have the opportunity to define architectural standards, deployment patterns, and internal tooling that will shape how AI capabilities are integrated and managed s is a platform engineering role with a strong emphasis on reliability, security, and scalability - areas where enterprise customers demand rigorous controls, observability, and fail‑safes before adopting AI solutions.

Your decisions will set the trajectory for future engineering efforts, ensuring that the next twenty models are deployed with greater efficiency than the first. The challenge lies in balancing the unpredictability of modern AI with the reliability expectations of enterprise environments. You will collaborate closely with data scientists and ML engineers to streamline workflows, eliminate redundant efforts, and establish best practices for model lifecycle management.

From infrastructure as code to security posture, your work will ensure that the platform meets the stringent requirements of regulated industries while remaining adaptable to evolving AI advancements. This is not a modeling role; it is an infrastructure role. Your expertise will be leveraged to solve complex problems at the intersection of software engineering and AI operations, where the stakes are high, and the impact is enterprise‑wide.

• Design and build the model serving layer, including real‑time inference, batch processing, and LLM endpoints, ensuring enterprise‑grade latency and availability requirements are met.
• Develop and implement CI/CD pipelines for models, covering automated training, testing, versioning, staged rollouts, and rollback mechanisms that integrate seamlessly into engineering workflows.
• Establish and maintain a comprehensive observability layer to monitor performance, model drift, data quality, cost efficiency, and output quality, ensuring proactive issue detection before customer impact.
• Own the evaluation infrastructure for AI features, including regression testing for non‑deterministic outputs and ensuring robust validation processes for production…