About the Role
The AI Engineer will ensure the reliability, performance, and continuous improvement of AI applications in production. The role combines day-to-day monitoring, incident response, and maintenance of live AI systems with active participation in designing and implementing new AI use cases.
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Responsibilities
- Monitor AI applications in production using observability tools (dashboards, logs, metrics, traces) to detect performance degradation, errors, or drift in model outputs.
- Respond to incidents and alerts, perform triage, root cause analysis, and coordinate resolution with development, infrastructure, and vendor teams.
- Maintain AI infrastructure, including inference endpoints, embedding services, vector databases, and supporting components.
- Perform routine health checks, apply patches and updates, manage secrets and configurations, and validate backups and recovery procedures.
- Document incidents, resolutions, and runbooks to build a knowledge base for recurring issues and standard operating procedures.
- Support SLAs for availability, latency, and throughput; track and report on key reliability metrics.
- Collaborate with solution architects, data engineers, and business stakeholders to gather requirements for new AI features and use cases.
- Assist in designing and implementing AI workflows, including prompt engineering, RAG pipelines, agent orchestration, and integration with enterprise systems.
- Develop and test APIs, microservices, and backend services to serve AI models and embed AI capabilities into applications.
- Participate in CI/CD for AI, including model and prompt versioning, automated testing, canary deployments, and safe rollback strategies.
- Contribute to LLMOps/MLOps practices, such as drift detection, evaluation frameworks, and observability instrumentation for new deployments.
Qualifications
- Bachelor’s degree in Computer Science, Electrical Engineering, Data Science, or a related field.
- 1–2 years of experience in software engineering, data engineering, DevOps, or AI/ML engineering.
- Hands-on experience with Python and SQL; familiarity with REST APIs and microservices.
- Exposure to production systems and operational responsibilities (monitoring, incident management, troubleshooting).
- Basic understanding of machine learning or generative AI concepts (models, embeddings, inference, prompts, RAG).
- Strong problem-solving skills, attention to detail, and ability to work under pressure during incidents.
- Good communication skills in English (Arabic is a plus) for documentation and cross-team collaboration.
Preferred Qualifications
- Experience with cloud platforms (Azure, AWS, or GCP) and AI services (e.g., Azure OpenAI, SageMaker, Vertex AI).
- Familiarity with vector databases (e.g., pgvector, Elasticsearch, Qdrant) and search systems (lexical, semantic, hybrid).
- Knowledge of LLMOps/MLOps tools for monitoring, evaluation, and deployment of AI models.
- Exposure to infrastructure as code (Terraform, Bicep) and CI/CD pipelines (GitHub Actions, Azure DevOps).
- Understanding of ITIL or similar service management frameworks for incident, change, and release management.
