Senior ML Ops Engineer
Role Overview
Deploy, operate, and maintain machine learning, generative AI, and agent-based solutions in production.
Build reliable deployment pipelines, monitor AI services, manage releases, resolve incidents, and maintain secure, scalable, cost-effective production systems.
Key Skills for This Role
Full Job Posting
Role overview
Deploy, operate, and maintain machine learning, generative AI, and agent-based solutions in production.
Build reliable deployment pipelines, monitor AI services, manage releases, resolve incidents, and maintain secure, scalable, cost-effective production systems.
Responsibilities
- Build and maintain production-grade MLOps and CI/CD pipelines.
- Deploy and manage models, APIs, AI agents, RAG workflows, and inference services.
- Monitor performance, model drift, data and response quality, security risks, and cloud costs.
- Investigate incidents, identify root causes, remediate issues, and improve post-incident processes.
- Maintain technical documentation, runbooks, release checklists, and deployment templates.
- Collaborate with AI, data, software, IT, security, and business teams.
Requirements
- 8+ years of relevant experience in MLOps, DevOps, platform engineering, data engineering, software engineering, or machine learning engineering.
- Strong experience with Python, SQL, Git, Docker, Kubernetes, MLflow, Terraform, REST APIs, Linux, Azure, Azure DevOps, Snowflake, FastAPI, Airflow, and Azure Data Factory.
- Hands-on deployment and support experience for containerized AI or machine learning services.
- Knowledge of generative AI, LLMOps, model registries, RAG workflows, vector stores, observability, and cloud security.
- Bachelor's or Master's degree in Computer Science, Engineering, Information Systems, or a related discipline.
- Strong analytical, troubleshooting, documentation, and communication skills.
Compensation
- Pay is AED 35,000–45,000 per month.
Work location
- Work location is in person in Dubai.
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