Machine Learning Engineer

🏢 epergne solutions
📍 Abu Dhabi, United Arab EmiratesFull-timeOn-site
📅 Posted: 3d ago🔄 Updated: 3d ago
CV%
✨ AI Summary
We are seeking a Machine Learning Engineer to design, develop, and deploy scalable ML/AI solutions for complex business problems in Abu Dhabi. This role involves building production-grade ML systems covering model development, deployment, optimization, and lifecycle management. Key responsibilities include working with large datasets, developing and optimizing various ML models (deep learning, neural networks, Agentic AI, SLM/LLMs), building end-to-end ML pipelines, and integrating models into production applications using CI/CD and MLOps practices. You will also develop model-serving APIs and collaborate with cross-functional teams to deliver AI solutions on AWS/Azure.
Required Skills
Other
modern ML AI frameworksML AI solutionsend-to-end ML pipelinesAgentic AILLM modelsML system architectures
Information Technology
Deep LearningMLflowCI/CDModel ServingProduction ML SystemsKubeFlow
Requirements
Requires 3-7 years of experience in building and deploying production-grade, scalable AI/ML systems. Must have strong expertise in supervised/unsupervised learning, deep learning, NLP, computer vision, or Generative AI/LLMs, along with knowledge of Python, modern ML/AI frameworks, Docker, Kubernetes, CI/CD, MLflow/Kubeflow, and experience deploying models on AWS or Azure. A Bachelor's degree in Computer Science, Engineering, or related field is mandatory.
Description
Job Role:-Machine Learning EngineerJob Location:- Abu Dhabi, UAEExperience:- 3-7 YearsRole 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
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🎯 Overalli74%
⚡ Skillsi85%
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Ontology Match: 85.0
Matched:✓ Requirements Matching✓ Ontology Skills Mapping
📜 Eligibilityi49%
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Local: 19600%
🏗️ Career Fiti91%
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Seniority: 91.0
📋 Requirementsi67%
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Domain: 67.0
🔥 Motivationi78%
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Title Fit: 78.00