Engineer - Data Scientist

We are looking for an Engineer for an AI company based in Dubai.

Location Dubai, UAE (Hybrid Work Model)

Qualifications:

• High expertise in programming languages such as Python and SQL.
• Hands-on experience working/deploying/executing AI and machine learning models and experience in integrating them into production environments.
• Hands-on experience working with OpenAI framework, building agents and RAGs.
• Hands-on experience with writing data crawling solutions.
• Experience with Computer Vision frameworks for image description.
• Familiarity with cloud platforms (AWS, GCP, or Azure)
• Familiar with ETL frameworks and tools for seamless data integration and transformation, including experience with platforms like dbt, Matillion and open-source alternatives.
• Strong problem-solving skills and ability to work in a fast-paced environment.

Key responsibilities:

Data Engineering & Data Science Integration:

• Use advanced statistical and machine learning methods to analyze large datasets and derive actionable insights, build predictive models and algorithms to address business challenges and support decision-making.
• Collaborate with domain experts to identify trends, patterns, and optimization opportunities.
• Develop clear and effective data visualizations and reports for stakeholders.
• Develop data models that align with business needs for efficient analytics and reporting.
• Monitor and enhance the performance of data systems, ensuring fast query execution and optimal resource use.
• Integrate data from various sources into centralized systems for seamless accessibility in analytics and data science workflows.

Data Engineering:

• Manage data storage solutions, including relational and NoSQL databases (e.g., SQL server, MongoDB, ElasticSearch, etc.).
• Build and optimize ETL workflows using tools like Apache Airflow, Spark, or dbt.
• Ensure data integrity, governance, and security best practices.

Data Science & AI:

• Implement NLP solutions, text analytics, and AI-powered automation tools.
• Develop machine learning models for predictive analytics, recommendation systems, and anomaly detection.
• Build autonomous AI agents to automate tasks in data analysis, marketing and operations.
• Implement LLMs, reinforcement learning, RAGs and prompt engineering to improve agent intelligence.
• Optimize Business Workflows connecting AI agents them with APIs and databases.
• Continuously explore new AI frameworks and multi-agent collaboration strategies.
• Develop AI models that learn, adapt, and enhance productivity over time.
• Optimize model performance using feature engineering, hyperparameter tuning, and A/B testing.
• Work closely with engineering teams to deploy AI/ML models in production.