About MadarMadar is a leading Saudi digital logistics platform, owned by Elm, transforming transportation management
across industries.
Madar connects shippers, carriers, and logistics partners through a unified ecosystem that improves visibility,
efficiency, automation, and shipment execution.
The platform enables seamless shipment management, financial integration, real-time tracking, and
operational visibility, helping businesses move freight with greater speed, transparency, and confidence.
About The RoleMadar is looking for an experienced and hands-on Mid-Senior AI Engineer to design, build, and
productionize AI capabilities across our logistics platform.
The role combines Generative AI, AI agents, Retrieval-Augmented Generation (RAG), intelligent
automation, and applied machine learning to solve real logistics and operational problems.
You will work closely with Product, Engineering, DevOps/Platform, Data, Security, and Operations teams to
take AI solutions from business requirements and prototypes into reliable production services.
The role will also help drive AI adoption across Madar's engineering organization by establishing reusable AI
development patterns, tools, standards, and engineering practices.
The ideal candidate combines strong software engineering fundamentals with hands-on experience building
production-grade AI and LLM applications.
Key ResponsibilitiesAI Product Development
- Design and build AI-powered logistics capabilities such as shipment assistants, customer-support assistants,
operational copilots, document intelligence, exception-handling workflows, and intelligent automation.
- Contribute to predictive AI use cases such as ETA prediction, anomaly detection, operational risk
identification, and shipment-related forecasting where appropriate.
- Translate product, business, and operational requirements into practical and reliable AI solutions
- Work closely with Product and Engineering teams to move AI capabilities from proof-of-concept to
production.
- Design reusable AI components and services that can be consumed across multiple Madar products and
engineering teams.
- Integrate AI services with Madar's existing Angular, Node.js, API, event-driven, and backend
platforms
LLM & RAG Engineering
- * Design, build, and maintain Retrieval-Augmented Generation pipelines using embeddings, vector
databases, structured data, unstructured documents, and logistics-domain information.
- Implement effective document ingestion, chunking, indexing, retrieval, reranking, and
context-management strategies.
- Build AI applications using leading LLM platforms such as Claude, OpenAI, Azure OpenAI, Gemini, or
equivalent enterprise AI platforms.
- Use AI frameworks such as LangChain, LangGraph, Semantic Kernel, LlamaIndex, or equivalent
frameworks where appropriate.
- Design model-agnostic architectures that allow Madar to evaluate and adopt different models based on
capability, reliability, latency, security, and cost.
- Improve retrieval relevance, grounding, response accuracy, and overall application reliability
- Design structured output and tool-calling patterns to reliably connect LLMs with business applications and
backend services.
AI Agents & Intelligent Automation
- Design and develop AI agents for logistics workflows such as dispatch assistance, customer support,
shipment exception handling, operations support, and internal productivity.
- Build reliable agentic workflows that combine LLM reasoning, deterministic business rules, APIs, enterprise
data, and external systems.
- Implement appropriate state management, memory, tool usage, workflow orchestration, and human
approval steps.
- Define boundaries between AI-driven decisions and deterministic application logic
- Design human-in-the-loop workflows where AI-generated decisions or actions require review or approval
- Ensure AI agents operate within clearly defined permissions and business boundaries
AI Evaluation & Quality Engineering
- * Design evaluation frameworks and test datasets for measuring AI system performance
- Evaluate solutions across dimensions including retrieval quality, groundedness, hallucination rate, task
completion and success rate, response quality, accuracy, latency, reliability, token consumption, and cost.
- Establish regression testing for prompts, retrieval pipelines, models, tools, and agent workflows
- Develop golden datasets and benchmark scenarios for critical Madar AI use cases
- Apply human evaluation, automated evaluation, and LLM-as-a-judge approaches where appropriate
- Continuously measure production AI performance and identify opportunities for improvement
AI Security, Governance & Responsible AI
- Apply secure AI engineering practices to protect Madar's customers, systems, and business data
- Implement controls against prompt injection, indirect prompt injection, sensitive information leakage,
unauthorized data access, excessive agent permissions, unsafe tool execution, and cross-user or
cross-tenant data exposure.
- Design secure authorization models for RAG applications and AI agents
- Ensure AI applications respect existing application-level access controls and data boundaries
- Apply secure secrets management and authentication when integrating AI services with internal and
external systems.
- Support auditability by implementing appropriate logging, tracing, and monitoring of AI interactions and
actions.
- Work with Security and Platform teams to establish practical AI governance and security standards
Engineering AI Adoption
- Drive practical adoption of Claude Code and other AI-assisted development tools across Madar
engineering teams.
- Develop reusable AI skills, subagents, prompt templates, engineering instructions, and development
workflows.
- Help engineers use AI effectively for coding, unit and integration testing, code reviews, debugging,
documentation, refactoring, application migrations, and infrastructure and automation tasks.
- Establish practical standards and engineering playbooks for AI-assisted software development
- Identify engineering workflows where AI can improve development velocity, quality, or operational
efficiency.
- Support engineering teams in using AI responsibly without compromising software quality, security, or
maintainability.
AI Platform, Production & MLOps
- * Design AI services for scalability, availability, maintainability, and production readiness
- Build APIs and reusable services that allow AI functionality to be consumed by different Madar applications
- Work with DevOps and Platform teams to establish CI/CD practices for AI applications
- Implement observability for AI services, including application tracing, model and prompt performance,
request latency, token usage, model cost, error rates, and retrieval performance.
- Monitor production AI applications and continuously identify operational or quality issues
- Optimize solutions based on quality, latency, throughput, infrastructure cost, and model/API cost
- Support versioning and controlled rollout of models, prompts, embeddings, retrieval strategies, and agent
workflows.
Applied Machine Learning
Where business use cases require traditional machine learning rather than LLM-based approaches:
- Build or support predictive models for use cases such as ETA prediction, anomaly detection, classification,
forecasting, or operational intelligence.
- Perform feature engineering and model evaluation using historical operational data
- Select appropriate statistical, machine-learning, or deep-learning techniques based on the problem rather
than defaulting to LLM-based solutions.
- Work with Data and Platform teams to operationalize ML models where required
Preferred Qualifications
- * Experience with Node.js / TypeScript and integration with modern web application architectures
- Experience with Angular or similar frontend frameworks is an advantage
- Experience with logistics, transportation, supply chain, e-commerce, or B2B technology platforms
- Experience implementing AI agents in production environments
- Experience with Claude Code or similar AI-assisted software-development platforms
- Experience with MLOps, AI observability, model lifecycle management, or AI production infrastructure
- Experience with traditional machine-learning frameworks such as scikit-learn, XGBoost, LightGBM, PyTorch,
or equivalent.
- Experience with OCR, document intelligence, document classification, or intelligent document processing
- Experience with fine-tuning, model customization, or parameter-efficient fine-tuning techniques
- Experience working with Azure AI services, Azure OpenAI, or other cloud-based AI platforms
- Experience implementing enterprise AI security, AI governance, or responsible-AI practices
- Experience designing AI solutions for multi-tenant, enterprise, or B2B SaaS platforms
What Success Looks Like
A successful AI Engineer in this role will:
- * Deliver AI capabilities that solve measurable logistics and operational problems
- Move AI solutions beyond prototypes into reliable, secure, and observable production services
- Build reusable AI patterns and services instead of isolated one-off implementations
- Establish measurable standards for AI quality, reliability, latency, and cost
- Enable engineering teams to use AI tools effectively and responsibly
- Help Madar develop a scalable AI engineering capability that can support multiple products and business
use cases over time.
Mid-Senior AI Engineer - Generative AI & Agents
Requirements
- * Bachelor's degree in Computer Science, Software Engineering, Artificial Intelligence, Data Science, or a
related technical field.
- 4+ years of software engineering experience, including approximately 1-2+ years of hands-on
experience building LLM or Generative AI applications.
- Strong programming skills in Python
- Strong understanding of software engineering principles, APIs, distributed systems, and production
application development.
- Practical experience building RAG applications, including embeddings, vector databases, document
processing, and retrieval techniques.
- Experience designing and developing AI agents, tool-calling systems, and multi-step LLM workflows
- Hands-on experience with AI frameworks such as LangChain, LangGraph, Semantic Kernel, LlamaIndex, or
equivalent.
- Hands-on experience with major LLM platforms such as Claude, OpenAI, Azure OpenAI, Gemini, or
equivalent.
- Experience integrating AI capabilities into production applications and backend services
- Understanding of AI evaluation, prompt testing, RAG evaluation, and AI application quality measurement
- Understanding of production AI concerns including reliability, latency, scalability, security, observability,
and cost.
- Strong product mindset with the ability to translate AI capabilities into practical business solutions
- Strong analytical and problem-solving capabilities
- Good communication skills and ability to collaborate across Product, Engineering, Data, Security, Platform,
and Operations teams
