Senior Technical Engineer (Data Science / Machine Learning)
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Key skills for this role
About the Role
Cinergie Digital is hiring a Senior Technical Engineer (Data Science / Machine Learning) to build and test AI/ML proofs of concept for real business requirements. The role rapidly prototypes models and agentic AI workflows, runs experiments, and delivers go/no-go recommendations.
Key Skills for This Role
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
Requirements
- 5–8 years in data science / machine learning roles
- 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
- Comfortable with ambiguity and fast iteration
- Strong communicator — able to explain methods, results, and go/no go calls to non technical stakeholders
Full Job Posting
Position Overview
- 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.
Compensation & Benefits
- Salary: AED 14,000 – AED 17,000 / month
- Benefits: Work visa, air tickets, medical insurance, gratuity, paid time off
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