Senior Data & Applied AI Engineer

almentor

. Architect, implement, and maintain pipelines that ingest, transform, and serve data for product features, analytics, and AI workflows .

Posted 10/22/2026full-timeCairo • EgyptSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in architecting and maintaining data pipelines, implementing LLM-powered features, and optimizing data processing for analytics and AI workflows. Strong capabilities in SQL, data modeling, and search/indexing systems are essential for delivering high-quality, reliable AI solutions.

Highest-signal resume keywords

Data Pipeline ArchitectureLLM-Powered Feature ImplementationSQL and Data ModelingSearch/Indexing Systems ExperienceCI/CD and Cloud Services Familiarity

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data EngineeringBackend EngineeringPlatform EngineeringSQLData ModelingBatch and Streaming PipelinesSearch/Indexing SystemsLLM ImplementationData Quality ValidationHybrid Search

Soft Skills

Problem-SolvingDebuggingWritten CommunicationVerbal Communication

Tools & Technologies

OpenSearchElasticsearchVector DatabasesDockerAWSGCP

Industry Keywords

Data ProcessingAI WorkflowsIncident ResponseContinuous ImprovementData Quality Monitoring

Tech Stack

Tools & technologies

AWSCloudDockerElasticSearchGoogle Cloud PlatformSQL

About the role

Key responsibilities & impact

Architect, implement, and maintain pipelines that ingest, transform, and serve data for product features, analytics, and AI workflows
Build structured and unstructured indexing and retrieval layers for search, recommendations, and AI assistants using RAG
Implement LLM-powered features including prompt orchestration, tool/function calling, RAG patterns, structured extraction, and summarization
Add policy checks, rate limits, cost controls, and safe handling of sensitive data
Create evaluation harnesses and regression tests for retrieval quality and AI outputs
Instrument pipelines and services with logging, tracing, metrics, and data quality monitoring
Partner with product managers, designers, and engineers to translate user needs into measurable data/AI capabilities
Participate in sprint planning, refinement, and cross-team alignment
Own production pipelines and services, including incident response, post-mortems, runbooks, and continuous improvement
Optimize latency, cost, and throughput across data processing, indexing, and AI inference
Design, oversee, build, and operate data pipelines and applied AI systems for consumer and business education products
Build and scale hybrid search, metadata filtering, and vector retrieval foundations
Deliver production LLM capabilities with measurable quality, cost controls, and safety guardrails
Improve performance, reliability, observability, and maintainability through proactive monitoring and data quality checks
Contribute to technical documentation and cross-team initiatives as a senior engineering voice

Requirements

What you’ll need

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 batch and/or streaming pipelines, including backfills and data quality validation
Experience with search/indexing systems and retrieval patterns, such as OpenSearch/Elasticsearch, vector databases, and hybrid search
Hands-on experience implementing LLM-powered features, including RAG, tool/function calling, and prompt/version management
Familiarity with CI/CD, Docker, and cloud services such as AWS or GCP
Strong problem-solving and debugging skills
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

Benefits

Comp & perks

Agile and XP methodologies
Collaboration, communication, and continuous improvement culture