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Senior Technical Engineer (Data Science / Machine Learning)

Cinergie Digital
Dubai, UAE
fulltime
Mid-Senior
AED 14,000 - 17,000/month
Yesterday
engineeringdesignproject managementmaintenancequality controltechnical
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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

Reference: FZ-AE-459

Cinergie Digital is 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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