Senior Technical Engineer (Data Science & ML)

Senior Technical Engineer (Data Science & ML) - Kamayi | Career Page

Senior Technical Engineer (Data Science & ML)

Dubai, United Arab Emirates

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Job Openings Senior Technical Engineer (Data Science & ML)

About the job Senior Technical Engineer (Data Science & ML)

Senior Technical Engineer (Data Science / Machine Learning)

Location

Dubai, UAE (Client Site)

Salary

AED 14,000 – AED 17,000 / month

Benefits

Work visa, air tickets, medical insurance, gratuity, paid time off

Experience

5–8 years of relevant experience

We're hiring a Senior Technical Engineer (Data Science / Machine Learning) to build and test AI and machine-learning proofs of concept scoped to real business requirements. The role rapidly prototypes models and agentic AI workflows, runs experiments to validate business hypotheses, and turns results into clear go/no-go recommendations. Work spans generative AI, agentic AI, and applied machine learning, with a focus on experimentation and fast iteration in a sandbox environment rather than production delivery.

Key Responsibilities

• Build and test AI and machine-learning proofs of concept scoped to real business requirements and hypotheses.
• Rapidly prototype models and agentic AI workflows, iterating quickly in a sandbox environment.
• Design and run experiments to validate business hypotheses and measure feasibility, accuracy, and performance.
• Apply generative AI and LLMs, including prompt engineering and retrieval patterns, to candidate use cases.
• Perform data wrangling and feature engineering across structured and unstructured data sources.
• Evaluate models and workflows against clear metrics, documenting findings, limitations, and trade-offs.
• Turn POC results into clear go/no-go recommendations and hand-off notes for stakeholders.

Required Technical Skills

• Strong Python for data science, with hands-on machine learning and deep learning.
• Practical experience with generative AI and LLMs, agentic AI frameworks, and prompt engineering.
• Data wrangling and feature engineering, plus solid SQL across relational data.
• Model evaluation and experimentation, grounded in applied statistics.
• Basic MLOps for experiment tracking, versioning, and reproducibility.

Candidate Profile

• 5–8 years in data science / machine-learning roles, with a track record of taking ideas to working POCs.
• Comfortable with ambiguity and fast iteration; biased toward experiments that produce clear answers.
• Strong communicator — able to explain methods, results, and go/no-go calls to non-technical stakeholders.

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