Senior Data & Applied AI Engineer
About almentor: almentor is a cutting-edge online video marketplace for e-learning and professional business development throughout the Middle East and Africa. Offering video courses in Arabic and English, we curate top-rated training programs of expert-created content and guided curriculum with interactive delivery.
Our Mission: almentor is on a mission to increase accessibility to affordable quality education for Arabic-speaking communities and a Goal to serve 10 million learners in MENA
Job Overview:
Reporting to the Head of Engineering, your role is to design, build, and operate the data and applied AI foundations that power intelligent learning experiences and business workflows. You will ship production-grade pipelines, retrieval/search systems, and LLM-enabled features that are measurable, secure, and reliable.
We are committed to building software that is not only functional and reliable, but also beautiful and intuitive. Our engineers and designers work together to create products that are a joy to use, and that solve real-world problems in innovative ways. We believe in the Agile and XP methodologies, and we prioritize collaboration, communication, and continuous improvement in everything we do.
We are embarking on an exciting journey of rebuilding our product and engineering organization to drive innovation and growth. Our goal is to create a world-class platform that delivers the best possible learning experiences to our users.
Share the journey along with engineers, product managers, and leaders from the most successful organizations in the region.
As a Senior Data / AI Engineer, you'll spend your time on:
- Blending data engineering, applied AI implementation, and operational ownership.
- Architect, implement, and maintain pipelines that ingest, transform, and serve data for product features, analytics, and AI workflows.
- Build indexing and retrieval layers (structured + unstructured) to power search, recommendations, and AI assistants (RAG).
- Implement LLM-powered features: prompt orchestration, tool/function calling, RAG patterns, structured extraction, summarization.
- Add guardrails: policy checks, rate limits, cost controls, and safe handling of sensitive data.
- Create evaluation harnesses and regression tests for retrieval of quality and AI outputs (offline datasets + automated checks).
- Instrument pipelines/services with logging, tracing, metrics, and data quality monitoring.
