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 will:
- As a Senior Data / AI Engineer, your days will blend data engineering, applied AI implementation, and
operational ownership. You’ll spend your time on: - 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.
Quality, Evaluation, and Observability. - 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.
- Partner closely with product managers, designers, and engineers to translate user needs into measurable data/AI capabilities.
- Participate in sprint planning, refinement, and cross-team alignment to keep execution predictable.
- Own your pipelines and services in production — incident response, post-mortems, runbooks, and continuous improvement.
- Optimize latency, cost, and throughput across data processing, indexing, and AI inference usage.
- A typical day might look like: checking pipeline freshness and alerts in the morning, tuning chunking/index mappings for a new course search mid-day, pairing with product on evaluation metrics for an AI study
- Assistant in the afternoon, and ending the day by shipping a monitored RAG endpoint with cost budgets and
regression tests.
Role Objectives
- Design, oversee, build and operate data pipelines and applied AI systems that power our education
platform across consumer and business customer segments. - Build and scale retrieval and search foundations (hybrid search, metadata filtering, vector retrieval)
that enable AI experiences. - Deliver production LLM capabilities with measurable quality, cost controls, and safety guardrails.
- Improve system performance, reliability, observability, and maintainability through proactive
monitoring and data quality checks. - Collaborate closely with stakeholders (product managers, designers, frontend/mobile engineers) to
translate business requirements into technical solutions. - Contribute to sprint planning, technical documentation, and cross-team initiatives as a senior
engineering voice. - Contributing to sprint planning, technical documentation, and cross-team initiatives as a senior voice
in the engineering org
The ideal candidate will have:
4+ years of experience in data engineering, backend engineering, or platform engineering, with meaningful production ownership.
Strong SQL and data modeling skills; experience designing schemas and data contracts.
Experience building and operating pipelines (batch and/or streaming), including backfills and data
quality validation.Experience with search/indexing systems and retrieval patterns (e.g., OpenSearch/Elasticsearch,
vector DBs, hybrid search).Hands-on experience implementing LLM-powered features (RAG, tool/function calling,
prompt/version management) — no deep ML required.
Familiarity with CI/CD, containerization (Docker), and cloud services (ex. AWS, GCP).
Strong problem-solving and debugging skills, with a proactive attitude towards owning and
improving systems.Strong written and verbal communication skills, with the ability to explain complex ideas clearly to
technical and non-technical stakeholders.Bachelor’s degree in computer science, Engineering, or a related field is preferred but not required
