Artificial Intelligence

• Audit manual workflows across accounting, purchasing, operations, customer service, marketing, catalog management, and reporting
• Identify, prioritize, build, and implement AI-powered automations
• Automate accounting workflows, beginning with credit card reconciliation
• Consolidate data from 7–8 sources into a reporting layer
• Automate invoice reconciliation between order management software and QuickBooks
• Own and improve an AI-powered customer-support ticketing agent
• Expand the support agent’s knowledge library and tune its auto-response success rate
• Clean and maintain a product catalog containing tens of thousands of SKUs
• Build tools or AI agents for product listing creation and catalog maintenance
• Connect sales history and inventory data to improve purchasing and reorder decisions
• Develop reporting on product sales velocity and inventory reorder timing
• Support non-technical team members with AI-powered solutions and enablement
• Use AI coding assistants such as Claude Code to build and ship internal tools
• Work directly with department leads to scope and prioritize automation projects
• Determine workflows, data sources, and prompts for each solution
• Own projects end to end, including audit, development, rollout, and adoption
• Work independently with minimal oversight
• Meet with every department during the first 30 days and deliver a prioritized automation roadmap to the founder

AI Workflow Specialist

• Research national policies, strategies, regulations, and government initiatives related to education, AI, digital transformation, and teacher development
• Analyze government and education-authority approaches to AI adoption and responsible use within education
• Identify policy developments, opportunities, constraints, and potential risks
• Map government entities, ministries, education authorities, regulators, universities, and other relevant institutions
• Identify stakeholders, decision-makers, influencers, and potential institutional champions
• Map government-led and institutional initiatives related to AI, digital education, and educator development
• Analyze barriers and enablers to institutional adoption
• Identify opportunities to align initiatives with national education and AI priorities
• Develop recommendations for government and institutional engagement
• Prepare concise briefing notes for stakeholder meetings
• Track policy and regulatory developments throughout the engagement
• Support the team in understanding pathways for long-term institutional adoption and sustainability
• Produce country-level policy and landscape briefs, stakeholder maps, strategy and policy mappings, opportunity/barrier/risk analyses, engagement recommendations, meeting briefing notes, regular updates, and strategic recommendations

Education, AI Policy Researcher

• Translate authentic Earth-science workflows into self-contained terminal-based benchmark tasks.
• Prepare and structure geospatial, climate, atmospheric, geological, hydrological, or oceanographic datasets.
• Build reproducible computational environments using scientific libraries and command-line tools.
• Develop expert reference solutions using Python, R, Bash, Julia, or domain-specific software.
• Design tasks involving geospatial analysis, time-series processing, numerical modeling, interpolation, forecasting, remote sensing, and environmental risk analysis.
• Define objective grading criteria for scientific outputs, data transformations, and spatial or temporal accuracy.
• Validate coordinate systems, units, timestamps, missing-data handling, and scientific assumptions.
• Create automated tests for numerical tolerances, file formats, metadata, and reproducibility.
• Debug issues involving projections, large datasets, dependencies, performance, and numerical stability.
• Document data provenance, expected outputs, edge cases, assumptions, and limitations.

Earth & Environmental Science Expert – AI Projects

• Design complex scientific workflows across computational life sciences that require multiple analytical and computational steps.
• Develop realistic scientific datasets, input files, instructions, constraints, and expected deliverables.
• Create tasks requiring AI agents to inspect data, select appropriate methods, execute analyses, and troubleshoot intermediate results.
• Implement reproducible expert solutions and objectively verifiable ground truths.
• Design robust automated or semi-automated grading criteria for scientific tasks.
• Validate scientific assumptions, calculations, code, intermediate outputs, and final results.
• Ensure task dependencies, data, and computational resources can be reliably packaged in controlled environments.
• Develop workflows using Python, command-line tools, scientific libraries, and relevant domain-specific software.
• Ensure tasks assess scientific reasoning and execution rather than memorization.
• Maintain high task quality and throughput while incorporating reviewer feedback.

Computational Biology, AI Specialist

• Present free AI diagnoses to CEOs or COOs as findings and convert credibility into paid Leadership Intensives
• Run the close with the account executive, owning the narrative and the ask
• Facilitate deep-dive sessions inside client systems and data to identify ranked AI opportunities
• Lead hands-on build sessions where executives create clickable first versions using their own data and AI tools
• Teach AI concepts, build-versus-buy-versus-hire considerations, and routine-versus-judgment distinctions
• Manage deliverables between sessions, including data exports and input from client leaders
• Deliver approximately 70% fixed, reusable Intensive content and 30% client-specific content
• Co-present 90-day technical roadmaps with senior engineers and close pod engagements
• Position client-managed pods, fully managed pods, on-call senior support, and credited Intensive fees correctly
• Handle objections regarding in-house hiring, nearshore talent, and implementation risk
• Provide weekly structured feedback to diagnosis systems, decks, playbooks, and close scripts
• Record sessions with consent, tag important moments, and support facilitator onboarding
• Help define hiring standards and train future facilitators
• Deliver two to three Intensive sessions, one or two diagnosis presentations, and one roadmap close in a typical week
• Run the full client-facing arc and meet conversion, session-quality, deal-durability, and machine-contribution targets

AI Enablement Facilitator

• Build a diligence data room for seller-uploaded incorporation documents, financials, and reports
• Build notifications identifying when a data room is complete or stalling
• Build a target-analysis tool for summarizing practice financial and business information
• Support a target-identification tool for finding practice owners to approach
• Connect meeting transcripts to the priority tracker as tentative tasks
• Implement human approval before tasks are added to the tracker
• Build a live brand rollout tracker for eight to ten specialty brands
• Track brand names, logos, colour systems, websites, signage, patient-facing collateral, ad creative, owners, statuses, and launch blockers
• Build a brand asset engine generating channel-specific advertising and design creative at volume
• Include human approval before publishing brand assets
• Build REST API integrations with OAuth2, pagination, rate limits, retries, and idempotency
• Work with the outsourced IT provider to obtain system access without disrupting the environment
• Develop with Microsoft 365, Graph API, SharePoint, Power Automate, Entra ID, and permission scopes
• Use Git and CI pipelines with tests before deployment
• Write, review, and debug production Python or TypeScript code
• Design and query SQL databases and reconcile disagreeing data sources
• Build React or equivalent front ends
• Deploy cloud applications, preferably on Azure
• Build production LLM workflows with structured outputs, tool calling, document retrieval, and prompt versioning
• Process PDFs and spreadsheets at volume, using OCR and extracting financial data
• Apply secrets management and least-privilege service accounts
• Later support cash-flow forecasting and intercompany reconciliation after the FP&A platform launches

AI Forward-Deployed Engineer

• Identify opportunities to apply data analytics and AI services across business functions and client engagements
• Evaluate business value, feasibility, and priority of opportunities and translate ambiguous needs into problem statements
• Develop proposals and RFP/RFI responses covering scope, approach, architecture, staffing, estimates, timelines, and expected impact
• Build investment business cases, including trade-offs and risks
• Lead client scoping and discovery workshops and manage scope changes and contract amendments
• Design custom data and AI solutions, including multi-agent pipelines, RAG systems, and LLM-powered workflows
• Own deployment into client and cloud environments, including CI/CD pipelines, containerization, environment setup, and post-deployment monitoring
• Define solution quality measures and build evaluation and validation loops for LLM and agent outputs
• Monitor production performance and drive continuous improvement based on results and user feedback
• Ensure solutions are maintainable, scalable, and reliable
• Manage AI workload cost and performance, including token and compute consumption, cloud spend, and latency
• Build solutions that respect data privacy, security, and responsible AI requirements
• Evaluate emerging data analytics, LLM, agentic framework, MLOps, and LLMOps technologies
• Define and champion technical standards and best practices with department and Tech/R&D leadership
• Lead projects end-to-end, coordinating priorities and allocating resources across concurrent initiatives
• Communicate progress, risks, dependencies, and trade-off decisions to leadership, clients, and stakeholders
• Plan team capacity and staffing and maintain delivery rituals, estimation practices, quality routines, and escalation paths
• Mentor and coach team members through training and career development
• Manage performance and development through 1:1s, feedback, performance conversations, objective setting, and career planning
• Contribute to hiring, onboarding, and retention
• Build a culture of technical rigour, intellectual curiosity, and shared ownership
• Uphold client confidentiality, data protection, information security, company policies, and responsible AI practices

Data & AI Solutions Lead

• Lead the delivery of the data platform workstream, from planning through production
• Translate the existing architecture and roadmap into clear technical deliverables and priorities
• Lead and support the team throughout the delivery
• Design and oversee data ingestion, transformation, modelling, and data quality activities
• Ensure the data platform is scalable, reliable, maintainable, and aligned with the agreed architecture
• Work closely with Project Managers to manage scope, priorities, dependencies, risks, and delivery progress
• Make and communicate technical decisions within the data engineering domain
• Review technical solutions and deliverables to ensure quality and consistency
• Identify technical and delivery risks early and drive them through to resolution
• Work with other technical teams to manage integrations and dependencies across the platform
• Help establish good data engineering practices that can be reused across future projects
• Act as the main technical point of contact for data delivery within the project

Artificial Intelligence jobs in Egypt