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. Job Summary 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 at scale. Unlike 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 at AI71. This 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. Key Responsibilities 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 models. Build and maintain infrastructure as code across all environments, adhering to the security and compliance standards required for enterprise and regulated customer deployments. Collaborate closely with data scientists and ML engineers to streamline the transition from notebook-based development to production, eliminating redundant rewrites and ensuring consistency. Define and enforce architectural standards, reference patterns, and internal tooling to optimize the deployment and operational efficiency of future models, reducing complexity for subsequent teams. Required Qualifications Substantial hands-on experience running machine learning systems in production, with a focus on platform engineering rather than model development. Strong expertise in Kubernetes and containerization, including ownership of cluster-level decisions and architecture rather than consuming pre-built solutions. Proven experience with infrastructure as code (Terraform or equivalent) and end-to-end CI/CD pipeline ownership for production systems. Deep familiarity with model serving frameworks such as KServe, Seldon, BentoML, vLLM, Triton, or comparable technologies. Solid software engineering skills in Python, with experience writing maintainable code for production environments. Hands-on experience with ML lifecycle tooling, including MLflow, Kubeflow, Airflow, feature stores, or equivalent custom-built solutions. Practical experience implementing monitoring and observability for ML systems, using tools like Prometheus, Grafana, or similar, with a focus on ML-specific failure modes and performance tracking. In-depth knowledge of cloud platforms (AWS, Azure, GCP, or sovereign cloud environments) and the ability to design scalable, secure architectures. Judgment to balance platform complexity with company stage, avoiding both fragile scripts and overly abstracted systems that may not align with current needs. Required Skills Substantial hands-on experience running machine learning systems in production, with a focus on platform infrastructure rather than model development Deep expertise in Kubernetes and containerization, including ownership of cluster-level architecture and decision-making Proficiency in Infrastructure as Code (Terraform or equivalent) and end-to-end CI/CD pipeline ownership for production environments Experience with model serving frameworks such as KServe, Seldon, BentoML, vLLM, or Triton, tailored for enterprise-scale deployments Strong software engineering skills in Python, with an emphasis on maintainable, production-grade code Familiarity with ML lifecycle tooling, including MLflow, Kubeflow, Airflow, or equivalent feature stores and pipeline orchestration systems Practical experience implementing monitoring and observability solutions (Prometheus, Grafana, or comparable) for ML-specific failure modes, including performance, drift, data quality, and output quality In-depth knowledge of cloud platforms (AWS, Azure, GCP, or sovereign cloud environments) and their application in AI/ML infrastructure Judgment in designing platform solutions that balance scalability, reliability, and adaptability to the company’s growth stage, avoiding over-engineering or fragile scripts Collaborative mindset to work closely with data scientists and ML engineers to streamline the transition from notebook-based development to production-ready deployments Work Location Details Location: Abu Dhabi, UAE Work Arrangement: On-site (hybrid option to be confirmed) Employment Type Employment Status: Permanent Work Arrangement: Full-time Team Overview: Platform Engineering This role will join the Platform Engineering team, a specialized group focused on designing, building, and maintaining the foundational infrastructure that enables seamless development, deployment, and operations across the organization. The team collaborates closely with engineering, product, and operations teams to ensure scalability, reliability, and efficiency of technical platforms. By leveraging modern engineering practices and technologies, Platform Engineering drives innovation while reducing operational overhead, allowing other teams to focus on delivering high-impact solutions. Reports To