{"id":1466384,"url":"https://alion.io/job/madarplatform-mid-senior-ai-engineer-generative-ai-agents","title":"Mid-Senior AI Engineer - Generative AI & Agents","company":{"id":720379,"name":"Madar","domain":"madarplatform.com","url":"https://alion.io/company/madarplatform","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":{"grade":"B","score":75,"open_postings":3,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-26T05:57:25Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Cairo, Egypt"],"countries":["EG"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":14000,"max_usd":37000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":2365},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Angular","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Claude","optional":false},{"name":"Claude Code","optional":false},{"name":"Elm","optional":false},{"name":"Embeddings","optional":false},{"name":"Gemini","optional":false},{"name":"Hallucination","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Node JS","optional":false},{"name":"OpenAI","optional":false},{"name":"RAG","optional":false},{"name":"Reranking","optional":false},{"name":"Semantic Kernel","optional":false},{"name":"Structured Outputs","optional":false},{"name":"Fine-tuning","optional":true},{"name":"JavaScript","optional":true},{"name":"LightGBM","optional":true},{"name":"OCR","optional":true},{"name":"Python","optional":true},{"name":"PyTorch","optional":true},{"name":"Scikit-learn","optional":true},{"name":"TypeScript","optional":true},{"name":"XGBoost","optional":true}],"status":"live","first_seen_at":"2026-09-29T14:59:30Z","employer_posted_date":"2026-09-29","last_verified_at":"2026-10-27T01:36:01Z","board_verified":true,"closed_at":null,"days_open":27,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":27},"description":"About Madar\nMadar is a leading Saudi digital logistics platform, owned by Elm, transforming transportation management\nacross industries.\nMadar connects shippers, carriers, and logistics partners through a unified ecosystem that improves visibility,\nefficiency, automation, and shipment execution.\nThe platform enables seamless shipment management, financial integration, real-time tracking, and\noperational visibility, helping businesses move freight with greater speed, transparency, and confidence.\nAbout the Role\nMadar is looking for an experienced and hands-on Mid-Senior AI Engineer to design, build, and\nproductionize AI capabilities across our logistics platform.\nThe role combines Generative AI, AI agents, Retrieval-Augmented Generation (RAG), intelligent\nautomation, and applied machine learning to solve real logistics and operational problems.\nYou will work closely with Product, Engineering, DevOps/Platform, Data, Security, and Operations teams to\ntake AI solutions from business requirements and prototypes into reliable production services.\nThe role will also help drive AI adoption across Madar's engineering organization by establishing reusable AI\ndevelopment patterns, tools, standards, and engineering practices.\nThe ideal candidate combines strong software engineering fundamentals with hands-on experience building\nproduction-grade AI and LLM applications.\nKey Responsibilities\nAI Product Development\nDesign and build AI-powered logistics capabilities such as shipment assistants, customer-support assistants,\noperational copilots, document intelligence, exception-handling workflows, and intelligent automation.\nContribute to predictive AI use cases such as ETA prediction, anomaly detection, operational risk\nidentification, and shipment-related forecasting where appropriate.\nTranslate product, business, and operational requirements into practical and reliable AI solutions.\nWork closely with Product and Engineering teams to move AI capabilities from proof-of-concept to\nproduction.\nDesign reusable AI components and services that can be consumed across multiple Madar products and\nengineering teams.\nIntegrate AI services with Madar's existing Angular, Node.js, API, event-driven, and backend\nplatforms\nLLM & RAG Engineering\nDesign, build, and maintain Retrieval-Augmented Generation pipelines using embeddings, vector\ndatabases, structured data, unstructured documents, and logistics-domain information.\nImplement effective document ingestion, chunking, indexing, retrieval, reranking, and\ncontext-management strategies.\nBuild AI applications using leading LLM platforms such as Claude, OpenAI, Azure OpenAI, Gemini, or\nequivalent enterprise AI platforms.\nUse AI frameworks such as LangChain, LangGraph, Semantic Kernel, LlamaIndex, or equivalent\nframeworks where appropriate.\nDesign model-agnostic architectures that allow Madar to evaluate and adopt different models based on\ncapability, reliability, latency, security, and cost.\nImprove retrieval relevance, grounding, response accuracy, and overall application reliability.\nDesign structured output and tool-calling patterns to reliably connect LLMs with business applications and\nbackend services.\nAI Agents & Intelligent Automation\nDesign and develop AI agents for logistics workflows such as dispatch assistance, customer support,\nshipment exception handling, operations support, and internal productivity.\nBuild reliable agentic workflows that combine LLM reasoning, deterministic business rules, APIs, enterprise\ndata, and external systems.\nImplement appropriate state management, memory, tool usage, workflow orchestration, and human\napproval steps.\nDefine boundaries between AI-driven decisions and deterministic application logic.\nDesign human-in-the-loop workflows where AI-generated decisions or actions require review or approval.\nEnsure AI agents operate within clearly defined permissions and business boundaries.\nAI Evaluation & Quality Engineering\nDesign evaluation frameworks and test datasets for measuring AI system performance.\nEvaluate solutions across dimensions including retrieval quality, groundedness, hallucination rate, task\ncompletion and success rate, response quality, accuracy, latency, reliability, token consumption, and cost.\nEstablish regression testing for prompts, retrieval pipelines, models, tools, and agent workflows.\nDevelop golden datasets and benchmark scenarios for critical Madar AI use cases.\nApply human evaluation, automated evaluation, and LLM-as-a-judge approaches where appropriate.\nContinuously measure production AI performance and identify opportunities for improvement.\nAI Security, Governance & Responsible AI\nApply secure AI engineering practices to protect Madar's customers, systems, and business data.\nImplement controls against prompt injection, indirect prompt injection, sensitive information leakage,\nunauthorized data access, excessive agent permissions, unsafe tool execution, and cross-user or\ncross-tenant data exposure.\nDesign secure authorization models for RAG applications and AI agents.\nEnsure AI applications respect existing application-level access controls and data boundaries.\nApply secure secrets management and authentication when integrating AI services with internal and\nexternal systems.\nSupport auditability by implementing appropriate logging, tracing, and monitoring of AI interactions and\nactions.\nWork with Security and Platform teams to establish practical AI governance and security standards\nEngineering AI Adoption\nDrive practical adoption of Claude Code and other AI-assisted development tools across Madar\nengineering teams.\nDevelop reusable AI skills, subagents, prompt templates, engineering instructions, and development\nworkflows.\nHelp engineers use AI effectively for coding, unit and integration testing, code reviews, debugging,\ndocumentation, refactoring, application migrations, and infrastructure and automation tasks.\nEstablish practical standards and engineering playbooks for AI-assisted software development.\nIdentify engineering workflows where AI can improve development velocity, quality, or operational\nefficiency.\nSupport engineering teams in using AI responsibly without compromising software quality, security, or\nmaintainability.\nAI Platform, Production & MLOps\nDesign AI services for scalability, availability, maintainability, and production readiness.\nBuild APIs and reusable services that allow AI functionality to be consumed by different Madar applications.\nWork with DevOps and Platform teams to establish CI/CD practices for AI applications.\nImplement observability for AI services, including application tracing, model and prompt performance,\nrequest latency, token usage, model cost, error rates, and retrieval performance.\nMonitor production AI applications and continuously identify operational or quality issues.\nOptimize solutions based on quality, latency, throughput, infrastructure cost, and model/API cost.\nSupport versioning and controlled rollout of models, prompts, embeddings, retrieval strategies, and agent\nworkflows.\nApplied Machine Learning\nWhere business use cases require traditional machine learning rather than LLM-based approaches:\nBuild or support predictive models for use cases such as ETA prediction, anomaly detection, classification,\nforecasting, or operational intelligence.\nPerform feature engineering and model evaluation using historical operational data.\nSelect appropriate statistical, machine-learning, or deep-learning techniques based on the problem rather\nthan defaulting to LLM-based solutions.\nWork with Data and Platform teams to operationalize ML models where required.\nPreferred Qualifications\nExperience with Node.js / TypeScript and integration with modern web application architectures.\nExperience with Angular or similar frontend frameworks is an advantage.\nExperience with logistics, transportation, supply chain, e-commerce, or B2B technology platforms.\nExperience implementing AI agents in production environments.\nExperience with Claude Code or similar AI-assisted software-development platforms.\nExperience with MLOps, AI observability, model lifecycle management, or AI production infrastructure.\nExperience with traditional machine-learning frameworks such as scikit-learn, XGBoost, LightGBM, PyTorch,\nor equivalent.\nExperience with OCR, document intelligence, document classification, or intelligent document processing.\nExperience with fine-tuning, model customization, or parameter-efficient fine-tuning techniques.\nExperience working with Azure AI services, Azure OpenAI, or other cloud-based AI platforms.\nExperience implementing enterprise AI security, AI governance, or responsible-AI practices.\nExperience designing AI solutions for multi-tenant, enterprise, or B2B SaaS platforms.\nWhat Success Looks Like\nA successful AI Engineer in this role will:\nDeliver AI capabilities that solve measurable logistics and operational problems.\nMove AI solutions beyond prototypes into reliable, secure, and observable production services.\nBuild reusable AI patterns and services instead of isolated one-off implementations.\nEstablish measurable standards for AI quality, reliability, latency, and cost.\nEnable engineering teams to use AI tools effectively and responsibly.\nHelp Madar develop a scalable AI engineering capability that can support multiple products and business\nuse cases over time.\nMid-Senior AI Engineer - Generative AI & Agents\nRequirements\nBachelor's degree in Computer Science, Software Engineering, Artificial Intelligence, Data Science, or a\nrelated technical field.\n4+ years of software engineering experience, including approximately 1-2+ years of hands-on\nexperience building LLM or Generative AI applications.\nStrong programming skills in Python.\nStrong understanding of software engineering principles, APIs, distributed systems, and production\napplication development.\nPractical experience building RAG applications, including embeddings, vector databases, document\nprocessing, and retrieval techniques.\nExperience designing and developing AI agents, tool-calling systems, and multi-step LLM workflows.\nHands-on experience with AI frameworks such as LangChain, LangGraph, Semantic Kernel, LlamaIndex, or\nequivalent.\nHands-on experience with major LLM platforms such as Claude, OpenAI, Azure OpenAI, Gemini, or\nequivalent.\nExperience integrating AI capabilities into production applications and backend services.\nUnderstanding of AI evaluation, prompt testing, RAG evaluation, and AI application quality measurement.\nUnderstanding of production AI concerns including reliability, latency, scalability, security, observability,\nand cost.\nStrong product mindset with the ability to translate AI capabilities into practical business solutions.\nStrong analytical and problem-solving capabilities.\nGood communication skills and ability to collaborate across Product, Engineering, Data, Security, Platform,\nand Operations 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