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Artificial Intelligence Engineer

Artificial Intelligence Engineer

Khatib & Alami
3-9 Years
Not Disclosed
  • Posted 17 hours ago
  • Be among the first 10 applicants

Job Description

Job Summary

The AI Engineer will design, develop, integrate, and deploy AI-powered applications and autonomous AI agents that improve business productivity, automate complex workflows, and enhance decision-making across the organization.

The role focuses on Agentic AI, building secure and scalable AI agents that can reason through tasks, retrieve and process information, interact with enterprise systems and tools, collaborate with other agents, and execute multi-step workflows with appropriate human oversight.

Key Responsibilities

  • Design and develop single-agent and multi-agent solutions for enterprise business processes.
  • Build agent architectures using LLMs, tool calling, workflows, memory, planning, reasoning, and retrieval-augmented generation (RAG).
  • Develop AI applications, copilots, assistants, and intelligent automation solutions integrated into existing business processes.
  • Identify opportunities to automate or augment repetitive activities across proposal and tender preparation, engineering QA/QC, project management, document control, contracts, finance, procurement, HR, and IT service management.
  • Develop secure integrations between AI agents and enterprise platforms through APIs, SDKs, web services, databases, and automation frameworks.
  • Build RAG and knowledge-engineering pipelines, including document ingestion, chunking, embeddings, metadata, indexing, retrieval, and reranking.
  • Develop production-quality AI solutions using Python, including APIs, microservices, backend services, and orchestration components.
  • Implement testing, CI/CD, version control, monitoring, logging, observability, and scalable deployment practices.
  • Apply appropriate governance, security, audit, privacy, and responsible AI controls, including safeguards against prompt injection, data leakage, and unauthorized actions.
  • Establish evaluation frameworks and continuously improve AI-agent accuracy, reliability, latency, cost efficiency, user adoption, and business outcomes.
  • Collaborate with business, IT, engineering, cybersecurity, data, and functional teams to translate business requirements into practical AI solutions.
  • Contribute to reusable AI platforms, standards, architecture patterns, and engineering practices.

Required Qualifications

  • Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Software Engineering, Data Science, Engineering, or a related discipline.
  • 3–7+ years of experience in software engineering, AI/ML, data engineering, or a related field, depending on seniority.
  • Strong programming skills in Python.
  • Strong understanding of LLMs, Generative AI, APIs, databases, distributed systems, and software architecture.
  • Hands-on experience building applications using LLM APIs, AI agents, AI workflows, or agentic applications.
  • Experience with Git, CI/CD, testing, and modern software-development practices.
  • Understanding of cloud or private-cloud deployment architectures.

Technical Skills

Essential

Python, LLM APIs and SDKs, prompt engineering, RAG, vector databases, agent orchestration, tool/function calling, workflow automation, API development and integration, REST APIs, SQL, Git, Docker, AI evaluation, and observability.

Desirable

LangGraph, LangChain, LlamaIndex, Semantic Kernel, MCP, Kubernetes, Azure, AWS, GCP or private-cloud platforms, knowledge graphs, multimodal AI, computer vision, OCR/document intelligence, event-driven architectures, SAP integration, SharePoint/Microsoft Graph, Autodesk Construction Cloud/BIM 360 APIs, and enterprise document-management systems.

Ideal Profile

The successful candidate will be a strong software engineer who can combine LLMs, enterprise data, APIs, workflows, and business processes to build reliable AI agents that perform meaningful work.

Experience within engineering, EPC, construction, or another complex enterprise environment, including systems such as SAP S/4HANA, SAP SuccessFactors, SAP Cloud for Customer, Microsoft SharePoint, Autodesk Construction Cloud/BIM 360, or enterprise project and document-management platforms, will be a significant advantage.

More Info

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Key Skills

tool function calling

vector databases

prompt engineering

LLM APIs

workflow automation

RAG

AI evaluation

SDKs

agent orchestration

observability

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