Senior AI Engineer

AI in this role

prompt-engineeringragai-agentsai-automationai-safety

Job Purpose

Responsible for designing, building, and operating production-grade AI agents, orchestration frameworks, and intelligent automation systems that power the organization’s customer, operational, and enterprise capabilities. The incumbent works closely with engineers and cross-functional teams to ensure AI solutions are accurate, reliable, and performant — forming the engineering foundation that scales the organization’s AI capabilities from discrete initiatives into a cohesive, enterprise-grade platform.

Key Result Responsibilities

AI Agent Design & Engineering

Design and build production-grade AI agents, multi-agent orchestration frameworks, and conversational systems.
Implement dialogue management, prompt engineering patterns, and context persistence architectures for scalable conversational experiences.

Intelligent Automation & Channel Delivery

Deliver AI-powered automation across customer interaction channels including chat, WhatsApp, email, and voice.
Integrate AI agents with reservation, pricing, and operational backend systems to enable end-to-end workflow automation.
Knowledge Systems & Retrieval
Build and operate retrieval-augmented generation (RAG) mechanisms enabling AI agents to utilize enterprise knowledge effectively.

Key Result Responsibilities-Continued

Production Quality & Reliability

Ensure AI solutions meet production standards for accuracy, latency, reliability, and safety.
Establish evaluation frameworks, testing environments, and model lifecycle practices for continuous improvement.

Collaboration & Engineering Excellence

Work closely with engineers, business, and operations teams to translate requirements into scalable AI capabilities.
Mentor engineers, champion best practices, and contribute through design reviews and architectural documentation.
Coach and guide junior engineers in AI engineering practices and promote a culture of excellence and continuous improvement.

Qualifications (Academic, training, languages)

Bachelor’s degree in Computer Science, Software Engineering, Artificial Intelligence, or a related field.
Strong proficiency in Python for building production-grade AI/ML systems.
Good understanding of agent workflows, tool usage, and execution patterns
Familiarity with Model Context Protocol (MCP).
Strong understanding of prompt engineering and structured prompting techniques.
Ability to implement context persistence and memory strategies.
Familiarity with multi-channel delivery (chat, WhatsApp, email, voice).
Ability to integrate enterprise knowledge sources (documents, APIs, structured data).
Familiarity with guardrails, fallback strategies, and failure handling.
Awareness of AI safety practices (e.g., prompt injection, output validation, guardrails).
Strong experience in building REST APIs and microservices.
Understanding of asynchronous processing and event-driven architecture.
Familiarity with CI/CD pipelines and cloud environments.
Understanding of scalability, reliability, and cost optimization basics.
Understanding of monitoring, logging, and pe.
Understanding of evaluation approaches for AI systems (testing, benchmarking)
Ability to optimize for latency, cost (token usage), and response quality.
Fluent in English Language.

Work Experience-Internal

With 2+ years of hands-on experience building production GenAI systems.
Hands-on experience designing AI agents and multi-agent orchestration systems.
Experience designing multi-turn conversational systems and dialogue flows.
Experience with Human-in-the-Loop (HITL) workflows.
Experience with vector databases and semantic search.
Hands-on experience building RAG pipelines.
Experience integrating AI with enterprise backend systems.
Experience with LLM APIs, SLMs, and agent frameworks.
Experience with Docker and Kubernetes.
Experience working with production-grade AI systems.

Work Experience

With 2+ years of hands-on experience building production GenAI systems.
Hands-on experience designing AI agents and multi-agent orchestration systems.
Experience designing multi-turn conversational systems and dialogue flows.
Experience with Human-in-the-Loop (HITL) workflows.
Experience with vector databases and semantic search.
Hands-on experience building RAG pipelines.
Experience integrating AI with enterprise backend systems.
Experience with LLM APIs, SLMs, and agent frameworks.
Experience with Docker and Kubernetes.
Experience working with production-grade AI systems.

How we rate this

Senior AI Engineer at Air Arabia rates 98 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

Prompt EngineeringRAGAI AgentsAI AutomationAI Safety

Questions you could be asked

How do you structure and test a prompt to get consistent output from a language model?
How would you design a retrieval step so the model answers from real data instead of guessing?
How do you decide when an AI agent can act on its own versus asking for approval first?
Tell me about a workflow you automated with AI tools, end to end.
How do you think about the risk of an AI system in this kind of role failing silently?

Adapt your resume

List these exact terms on your resume: Prompt Engineering, RAG, AI Agents, AI Automation, and AI Safety. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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