Senior Data & Applied AI Engineer

About the role

almentor is an online video marketplace for e-learning and professional business development in the Middle East and Africa, offering video courses in Arabic and English. Reporting to the Head of Engineering, you will design, build and operate the data and applied AI foundations behind intelligent learning experiences and business workflows, shipping production-grade pipelines, retrieval and search systems, and LLM-enabled features that are measurable, secure and reliable.

Key responsibilities

• Architect, implement and maintain pipelines that ingest, transform and serve data for product features, analytics and AI workflows.
• Build indexing and retrieval layers (structured and unstructured) for search, recommendations and AI assistants (RAG).
• Implement LLM-powered features such as prompt orchestration, tool/function calling, RAG patterns, structured extraction and summarization.
• Add guardrails: policy checks, rate limits, cost controls and safe handling of sensitive data.
• Create evaluation harnesses and regression tests for retrieval quality and AI outputs, using offline datasets and automated checks.
• Instrument pipelines and services with logging, tracing, metrics and data quality monitoring.
• Partner with product managers, designers and engineers to turn user needs into measurable data and AI capabilities, and take part in sprint planning and refinement.
• Own pipelines and services in production, including incident response, post-mortems and runbooks.
• Optimize latency, cost and throughput across data processing, indexing and AI inference.

Required profile

• 4+ years of experience in data engineering, backend engineering or platform engineering, with meaningful production ownership.
• Bachelor's degree in computer science, engineering or a related field preferred but not required.
• Strong SQL and data modeling skills, with experience designing schemas and data contracts.
• Experience building batch and/or streaming pipelines, including backfills and data quality validation.
• Experience with search and indexing systems and retrieval patterns (OpenSearch/Elasticsearch, vector databases, hybrid search).
• Hands-on experience implementing LLM features (RAG, tool/function calling, prompt/version management); deep ML knowledge not required.
• Familiarity with CI/CD, Docker and cloud services (e.g. AWS, GCP).
• Strong problem-solving, debugging and communication skills, able to explain complex ideas to technical and non-technical stakeholders.