Senior Technical Engineer (Data Science & ML)

Kamayi; Dubai; On-site; AED 156,240 - 189,720; Full time

Benefits offered by this job

Work visa

Air tickets

Medical insurance

Gratuity

Paid time off

Job summary

Kamayi is looking for a Senior Technical Engineer (Data Science / Machine Learning) in Dubai. The role involves building AI proofs of concept, rapid prototyping, and validating business hypotheses through experimentation.

Ideal candidates have 5-8 years of experience, strong Python skills, and practical knowledge of generative AI. The position offers a salary between AED 14,000 – AED 17,000/month along with benefits such as medical insurance and paid time off.

Qualifications

• 5–8 years of experience in data science or machine learning roles.
• Hands-on experience with AI and machine-learning proofs of concept.
• Comfortable with ambiguous situations and fast iterations.

Responsibilities

• Build and test AI and machine-learning proofs of concept for real business requirements.
• Rapidly prototype models and workflows in a sandbox environment.
• Apply generative AI and LLMs to candidate use cases.
• Evaluate models against clear metrics and document findings.

Skills

Strong Python for data science

Machine learning and deep learning

Generative AI and LLMs

Data wrangling

Feature engineering

SQL across relational data

Model evaluation and experimentation

Basic MLOps

Job description

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.