Senior MLOps / LLMOps Engineer

Company Description MENA Engineers connects global hiring teams with rigorously assessed technical professionals from across the Middle East and North Africa. The company focuses on replacing crowded talent marketplaces with carefully curated, evidence-based introductions, supported by structured, human-led vetting of identity, communication, practical skills, experience, and references. Candidate privacy is a core principle, with profiles kept off public platforms and identifying information shared only after the candidate approves a specific opportunity. Current specializations span mobile engineering, backend and cloud, frontend development, QA automation, data and AI, and product design, enabling global teams to tap into highly vetted regional talent. Professionals vetted by MENA Engineers join international organizations ready to contribute to distributed, high-performance teams.
Role Description This is a remote, contract role for a Senior MLOps / LLMOps Engineer supporting global clients through MENA Engineers. The person in this role will design, implement, and maintain production-grade machine learning and large language model pipelines, including data ingestion, training, evaluation, deployment, and monitoring. Day-to-day responsibilities include building and optimizing CI/CD workflows for ML systems, managing model lifecycle and versioning, and ensuring reliability, scalability, and cost efficiency of cloud-based ML infrastructure. The engineer will collaborate with data scientists, ML engineers, and product teams to operationalize models, develop APIs and microservices for inference, and integrate observability, testing, and governance into ML and LLM workflows. They will also contribute to best practices, documentation, and technical guidance to help teams adopt robust MLOps and LLMOps processes.
Qualifications
Strong experience in machine learning operations, including designing and maintaining ML pipelines, model deployment, monitoring, and lifecycle management.
Hands-on expertise with LLMOps, such as integrating large language models into applications, managing prompt workflows, fine-tuning or retrieval-augmented generation, and monitoring LLM performance and safety.
Proficiency with cloud platforms (e.g., AWS, GCP, Azure) and containerization/orchestration tools (e.g., Docker, Kubernetes) for scalable ML infrastructure.
Solid background in CI/CD, automation, and infrastructure-as-code (e.g., GitHub Actions, GitLab CI, Jenkins, Terraform) for ML and data systems.
Experience with ML and data tooling (e.g., Python, ML frameworks, experiment tracking, feature stores, data pipelines, and observability tools).
Strong software engineering fundamentals, including APIs and microservices, testing strategies, code quality, and secure, reliable production systems.
Ability to work effectively in remote, distributed teams, communicate clearly with technical and non-technical partners, and document processes and architectures.

Work arrangement
Yes