Senior Specialist, Data Science & Artificial Intelligence II
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Job Purpose
Design, develop, and deploy machine learning and Generative AI solutions to solve defined business problems, ensuring technical robustness, model performance, and responsible AI implementation.
Key Accountabilities
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Develop, test, and validate machine learning, deep learning, and Generative AI models (including LLM-based applications).
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Implement prompt engineering strategies and retrieval-augmented generation (RAG) pipelines.
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Perform feature engineering, model tuning, and performance optimization.
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Prepare, cleanse, and transform structured and unstructured datasets.
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Support deployment of ML and GenAI models using MLOps and LLMOps practices.
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Conduct structured evaluation of LLM outputs including hallucination detection and quality scoring.
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Integrate AI solutions via APIs into enterprise systems.
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Document model design, assumptions, risks, and validation results.
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Ensure compliance with data governance, cybersecurity, and ethical AI guidelines.
⢠Support monitoring, retraining, and lifecycle management of deployed models.
Minimum Qualification, Experience and Competencies
⢠Minimum Qualification
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Bachelorās degree in Data Science, Artificial Intelligence, Computer Science, or a related quantitative field
Minimum Experience
⢠4ā6 years in data science, machine learning, or applied AI roles.
Skills:
⢠Python, SQL, ML frameworks (TensorFlow, PyTorch, Scikit-learn)
⢠Prompt engineering
⢠RAG pipelines & vector databases
⢠Model fine-tuning and evaluation
⢠Data preprocessing & feature engineering
⢠Basic cloud deployment concepts
⢠MLOps / LLMOps fundamentals
⢠Analytical problem-solving
⢠Results Orientation
⢠Collaboration
⢠Continuous Improvement
⢠Accountability
Requirements
- ā¢Bachelorās degree in Data Science, AI, Computer Science, or related quantitative field
- ā¢4ā6 years in data science, machine learning, or applied AI roles
- ā¢Proficiency in Python and SQL
- ā¢Experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn)
- ā¢Knowledge of prompt engineering and RAG pipelines
- ā¢Familiarity with vector databases, model fine-tuning, and evaluation
- ā¢Understanding of data preprocessing and feature engineering
- ā¢Basic cloud deployment concepts and MLOps/LLMOps fundamentals
Nice to Have
- ā¢Ethical AI guidelines compliance
- ā¢Monitoring, retraining, and lifecycle management of deployed models
Responsibilities
- ā¢Develop, test, and validate ML, deep learning, and Generative AI models
- ā¢Implement prompt engineering strategies and RAG pipelines
- ā¢Perform feature engineering, model tuning, and performance optimization
- ā¢Prepare, cleanse, and transform structured and unstructured datasets
- ā¢Support deployment of ML and GenAI models using MLOps and LLMOps
- ā¢Evaluate LLM outputs, including hallucination detection and quality scoring
- ā¢Integrate AI solutions via APIs into enterprise systems
- ā¢Document model design, assumptions, risks, and validation results
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- Find what's costing you interviews at Ma'aden
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- Download Gulf-ready CV
60 seconds. $3.99 one-time.
Ma'aden is a Saudi Arabian mining and metals company developing the country's mineral resources. They focus on producing aluminum, gold, phosphates, and other industrial minerals.
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