AI & Data Intelligence Engineer

AI & Data Intelligence Engineer

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Job Description

Since 1974, S.K.M Air Conditioning LLC has been a trusted leader in HVAC manufacturing across the Middle East and beyond. With 2,000+ employees representing 30+ nationalities, SKM operates in 8+ count...

Since 1974, S.K.M Air Conditioning LLC has been a trusted leader in HVAC manufacturing across the Middle East and beyond. With 2,000+ employees representing 30+ nationalities, SKM operates in 8+ countries and delivers solutions to 30+ global markets. We pair engineering excellence with digital innovation-modern data platforms, AI/ML, and automation to elevate product quality, customer experience, and operational efficiency.

Why Join SKM?

At SKM, you'll join a transformation-driven environment where AI, data intelligence, and automation directly shape business outcomes. You'll partner with the Head of Digital Transformation & BPM and cross-functional teams to design and deploy AI/ML & GenAI solutions, autonomous agents (MCP), and Power BI analytics that drive smarter decisions, boost productivity, and reduce cost across manufacturing, supply chain, and corporate functions.

If you're passionate about building scalable data pipelines, productionizing models on Azure, and turning insights into action, SKM offers the platform, support, and impact to do your best work.

Role Overview

We're hiring an AI & Data Intelligence Engineer to turn data into action. You'll design and productionize ML/GenAI solutions and autonomous AI agents (with MCP for contextual integrity), build scalable data pipelines, and deliver Power BI analytics that drive smarter decisions, productivity gains, and cost reduction across SKM.

Key Responsibilities

  • Design, train, and deploy AI/ML & GenAI models and autonomous agents that analyze data, generate recommendations, and execute actions where appropriate.
  • Build and maintain ETL/data pipelines to ingest, transform, and serve data from diverse sources for AI and analytics use cases.
  • Apply statistical modeling, machine learning, RAG, and fine-tuning/transfer learning to uncover trends and automate workflows.
  • Develop interactive Power BI dashboards and advanced DAX models to communicate insights to business stakeholders.
  • Integrate AI solutions into business processes in partnership with product managers, analysts, and leadership-leveraging MCP to preserve context across actions.
  • Enforce data quality, integrity, security, and governance; monitor model accuracy, bias, and ethical use.
  • Operate in Azure (and related cloud services) to package, deploy, and monitor models and agents at scale.
  • Document solutions, share best practices, and provide L2/L3 support for AI systems, pipelines, dashboards, and MCP-enabled automations.
  • Stay current with emerging tools and methods in AI/ML, GenAI, analytics, and autonomous agents.

Qualifications

  • Bachelor's in Computer Science, Data Science, AI/ML, Statistics, or related field (advanced degree/certifications in AI/ML/Analytics are a plus).
  • 2+ years in data analytics or AI/ML with delivered, real-world AI-driven solutions (insights and/or automation).
  • Strong programming in Python (plus SQL; R is a plus).
  • Hands on with Power BI (data modeling & DAX) and building decision grade dashboards.
  • Experience designing ETL/data pipelines and deploying models/agents to production.
  • Familiarity with autonomous AI agents, MCP (Model Context Protocol), and RAG patterns.
  • Proficiency with AI frameworks/tools such as TensorFlow, PyTorch, OpenAI API, Hugging Face, LangChain, Microsoft Copilot Studio, and Azure AI services.
  • Understanding of data governance, security, and compliance; ability to align solutions to business processes (manufacturing, supply chain, finance).

Core Behaviours

Results orientation Teamwork & delegation Problem solving & decision making Planning & organizing Communication & relationship building Analytical thinking

  • On time, on budget delivery of AI projects
  • Accuracy/impact of insights and recommendations; automation success rate
  • Reliability/scalability of data pipelines and deployed models/agents
  • Stakeholder adoption, satisfaction, and measurable productivity/cost improvements
  • Quality documentation, knowledge sharing, and innovation contributions

Why SKM? Join a transformation focused team integrating AI across the business to deliver tangible value at scale.

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