Machine Learning Engineer

Epergne Solutions; Dubai; On-site; AED 180,000 - 300,000; Full time

Job summary

Epergne Solutions in Abu Dhabi seeks a Machine Learning Engineer to design, develop, and deploy scalable ML/AI solutions for complex business problems. You will build production-grade ML systems covering model development, deployment, optimization, and lifecycle management.

The role requires 3–7 years of experience, strong Python and ML frameworks, and hands-on expertise with MLOps, Docker/Kubernetes, and cloud platforms such as AWS or Azure.

Qualifications

• 3-7 years of experience building and deploying production-grade, scalable AI/ML systems.
• Strong expertise in supervised/unsupervised learning, deep learning, NLP, computer vision, or Generative AI/LLMs
• Strong understanding of ML system architecture and model optimization
• Experience with Python and modern ML/AI frameworks
• Knowledge of Docker, Kubernetes, CI/CD, MLflow/Kubeflow or similar MLOps tools
• Experience with model serving, APIs, and production deployment
• Hands-on experience deploying ML models on AWS or Azure
• Bachelor's degree in Computer Science, Engineering, or related field
• Master's/PhD in Computer Science or related field is preferred
• Banking Exposure is preferred

Responsibilities

• Design, develop, and deploy scalable ML/AI solutions for complex business problems.
• Build production-grade ML systems covering model development, deployment, optimization, and lifecycle management.
• Work with large and complex datasets to develop data-driven solutions.
• Develop, train, fine-tune, and optimize ML, deep learning, neural network, Agentic AI, SLM/LLM models.
• Build end-to-end ML pipelines covering data ingestion, transformation, training, validation, and deployment.
• Develop and optimize real-time and batch inference solutions.
• Build ML system architectures and integrate models into production applications.
• Automate model training, testing, deployment, and monitoring using CI/CD and MLOps practices.
• Develop model-serving APIs using FastAPI/Flask.
• Collaborate with cross-functional engineering and business teams to deliver end-to-end AI solutions.
• Deploy and manage ML solutions on AWS/Azure.

Skills

ML system design

Python

ML frameworks

Docker/Kubernetes

CI/CD

AWS/Azure

NLP/Generative AI

REST APIs

Education

Bachelor's degree

Master's/PhD preferred

Tools

Docker

Kubernetes

MLflow/Kubeflow

FastAPI/Flask

Job description

• Design, develop, and deploy scalable ML/AI solutions for complex business problems.
• Build production-grade ML systems covering model development, deployment, optimization, and lifecycle management.

Job Role

Machine Learning Engineer

Job Location

Abu Dhabi, UAE

Experience

3-7 Years

Role Overview

• Design, develop, and deploy scalable ML/AI solutions for complex business problems.
• Build production-grade ML systems covering model development, deployment, optimization, and lifecycle management.

Key Responsibilities

• Work with large and complex datasets to develop data-driven solutions.
• Develop, train, fine-tune, and optimize ML, deep learning, neural network, Agentic AI, SLM/LLM models.
• Build end-to-end ML pipelines covering data ingestion, transformation, training, validation, and deployment.
• Develop and optimize real-time and batch inference solutions.
• Build ML system architectures and integrate models into production applications.
• Automate model training, testing, deployment, and monitoring using CI/CD and MLOps practices.
• Develop model-serving APIs using FastAPI/Flask.
• Collaborate with cross-functional engineering and business teams to deliver end-to-end AI solutions.
• Deploy and manage ML solutions on AWS/Azure.

Requirements & Skills

• 3-7 years of experience building and deploying production-grade, scalable AI/ML systems.
• Strong expertise in supervised/unsupervised learning, deep learning, NLP, computer vision, or Generative AI/LLMs.
• Strong understanding of ML system architecture and model optimization.
• Experience with Python and modern ML/AI frameworks.
• Knowledge of Docker, Kubernetes, CI/CD, MLflow/Kubeflow or similar MLOps tools.
• Experience with model serving, APIs, and production deployment.
• Hands-on experience deploying ML models on AWS or Azure.
• Bachelor's degree in Computer Science, Engineering, or related field.
• Master's/PhD in Computer Science or related field is preferred.
• Banking Exposure is preferred