An AI-forward organisation in Khalifa City, Abu Dhabi is seeking an experienced MLOps Engineer on a contract, on-site basis — to develop, deploy, and maintain machine learning models and data-driven solutions at production scale. This technically comprehensive role requires 3–7+ years of Data Science, Machine Learning, or MLOps experience, strong Python, SQL, and Spark programming skills, hands-on proficiency with TensorFlow, PyTorch, MLflow, LangChain, LlamaIndex, and deep understanding of MLOps and LLMOps architectures — deployed on Microsoft Azure and Databricks with Azure DevOps CI/CD practices.
About This MLOps Engineering Opportunity
Location: Khalifa City, Abu Dhabi — on-site contract engagement in one of the UAE’s fastest-growing AI and technology districts
Contract Scope: Full-cycle MLOps — model development, pipeline architecture, deployment, monitoring, LLMOps, and model lifecycle management
Tech Stack: Python, SQL, Spark, Azure, Databricks, TensorFlow, PyTorch, MLflow, LangChain, LlamaIndex, Docker, Azure DevOps
Collaboration: Work directly with Data Scientists, Data Engineers, and Software Engineers to productionise ML models at enterprise scale
Why This MLOps Engineer Role Stands Out
LLMOps Architecture Scope: Support both MLOps and LLMOps architectures — applying LangChain and LlamaIndex to production LLM pipeline management at enterprise scale
Full ML Lifecycle Ownership: From data preprocessing and feature engineering through model training, deployment, CI/CD, containerisation, and production monitoring
Azure + Databricks Stack: Work with Microsoft Azure and Databricks — two of the most in-demand enterprise ML cloud platforms in the UAE market
Khalifa City Abu Dhabi: On-site contract in one of Abu Dhabi’s most strategically important and rapidly growing technology and innovation locations
Position Overview
This MLOps Engineer in Khalifa City, Abu Dhabi develops, deploys, and maintains machine learning models and data-driven solutions, works with large and complex datasets to support AI and ML use cases, performs data preprocessing, feature engineering, and model evaluation, builds and maintains scalable ML pipelines and deployment workflows, supports MLOps and LLMOps architecture, automation, and model lifecycle management, collaborates with Data Scientists, Data Engineers, and Software Engineers to productionise ML models, implements CI/CD practices for machine learning workflows, monitors model performance and reliability in production environments, supports containerised ML workloads using Docker and cloud technologies, and contributes to technical documentation, development standards, and engineering best practices.
Why This Role Matters: As MLOps Engineer in Khalifa City Abu Dhabi, you build and maintain the production infrastructure that transforms machine learning research into business reality — designing the scalable pipelines, CI/CD workflows, and LLMOps architectures that allow AI models to be deployed reliably, monitored consistently, and improved continuously at enterprise scale. Combining Python, Spark, Azure, Databricks, TensorFlow, LangChain, and Docker expertise, you are the critical link between data science innovation and production-grade AI delivery in one of the UAE’s most ambitious technology markets.
Key Responsibilities
ML Model Development, Deployment & Pipeline Architecture
- Develop, deploy, and maintain machine learning models and data-driven solutions across the full ML lifecycle
- Build and maintain scalable ML pipelines and deployment workflows using Python, Spark, MLflow, and Azure-native tools
- Perform data preprocessing, feature engineering, and model evaluation on large, complex datasets for AI and ML use cases
- Support containerised ML workloads using Docker and cloud-based development environments on Microsoft Azure and Databricks
MLOps & LLMOps Architecture
- Support MLOps and LLMOps architecture design, automation, and model lifecycle management across the enterprise AI stack
- Implement CI/CD practices for machine learning workflows using Azure DevOps — enabling reliable, automated ML model delivery
- Apply LangChain and LlamaIndex for LLMOps pipeline management — supporting production-grade LLM integration and orchestration
- Monitor model performance, reliability, and production environments — identifying and resolving model drift, data quality, and infrastructure issues proactively
Cross-Functional Collaboration & Technical Standards
- Collaborate with Data Scientists, Data Engineers, and Software Engineers to productionise ML models effectively and reliably
- Work with large, complex datasets on Databricks and Azure cloud data platforms to support AI and machine learning use cases
- Apply strong knowledge of NLP, Deep Learning, and Computer Vision techniques in production ML engineering contexts
- Contribute to technical documentation, development standards, and ML engineering best practices across the team
Required Experience & Qualifications
Essential Requirements
- 3–7+ years of experience in Data Science, Machine Learning, or MLOps — with proven production deployment track record
- Strong programming skills in Python, SQL, and Spark
- Hands-on experience with ML frameworks and tools — TensorFlow, PyTorch, Spark MLlib, MLflow, LangChain, or LlamaIndex
- Strong knowledge of machine learning techniques — NLP, Deep Learning, and Computer Vision
- Strong understanding of MLOps and LLMOps architectures and deployment patterns
- Experience working with Docker and cloud-based development environments
- Familiarity with Databricks and Microsoft Azure cloud data platforms
- Experience with Azure DevOps and CI/CD practices for ML workflows
- Experience working with large, complex datasets in production ML environments
About This MLOps Engineering Opportunity — Khalifa City Abu Dhabi
Join an AI-driven organisation in Khalifa City, Abu Dhabi as MLOps Engineer on a contract, on-site basis — building and maintaining the production machine learning infrastructure that powers data-driven decision-making at enterprise scale. Applying Python, Spark, Azure, Databricks, TensorFlow, PyTorch, LangChain, and Docker expertise, you will productionise ML models, build scalable pipelines, implement LLMOps architecture, and monitor production AI systems — contributing to one of the UAE’s most technically ambitious and fast-moving AI deployment programmes at a time when Abu Dhabi is establishing itself as a global leader in artificial intelligence infrastructure and capability.
Career Excellence: Build and maintain production ML and LLMOps pipelines in Abu Dhabi — Python, Azure, Databricks, LangChain, and Docker at enterprise AI scale.
Who Should Apply?
- MLOps Engineers & ML Platform Engineers: With 3–7+ years of experience building, deploying, and maintaining production ML pipelines on Azure and Databricks
- LLMOps Specialists: With hands-on LangChain, LlamaIndex, and LLM orchestration experience — deploying LLM-powered applications into production environments
- Python & Spark ML Engineers: With TensorFlow, PyTorch, MLflow, and NLP/Deep Learning/Computer Vision model production experience
- Azure & Databricks ML Engineers: With cloud-native ML pipeline, Azure DevOps CI/CD, and Docker container orchestration expertise for ML workloads
- Abu Dhabi-Based Contract Engineers: Available for immediate on-site engagement in Khalifa City — ready to contribute to production ML delivery from day one
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