AI Engineer

Dicetek LLC; Dubai; On-site; AED 200,000 - 420,000; Full time

Job summary

Dicetek LLC is seeking an AI/ML Engineer to drive the full lifecycle of AI initiatives in the UAE, from problem framing to deployment. You will partner with business units to define opportunities, design scalable architectures, and implement ML/LLM solutions while ensuring governance and security considerations.

You will lead data readiness assessments, evaluate frameworks, and oversee PoCs and pilots, guiding build-versus-buy decisions and production handover.

Qualifications

• Master's or PhD in Computer Science, Data Science, or a related field with strong AI focus.
• Proven experience of 3+ years developing and deploying ML models in professional settings.
• Proficient in Python, R or Java and familiar with ML frameworks such as TensorFlow, PyTorch, Scikit-learn.

Responsibilities

• AI Opportunity Assessment: scope business problems and translate into use cases and success measures.
• Technical Solution Design: define architecture, data needs, and integrations for AI solutions.
• AI Development: implement, configure and support ML/LLM solutions.
• Technical Feasibility: assess data readiness, infrastructure, and risks before development.
• AI Evaluation: establish test criteria and thresholds for model performance and safety.
• PoC Pilot Management: design and run PoCs with measurement and issue resolution.
• Vendor Evaluation: assess vendor claims and build-vs-buy decisions.
• Deployment: transition solutions into production with monitoring and handover.
• Performance Monitoring: set KPIs and continuously improve deployed AI systems.
• Project Lifecycle Support: track milestones and resolve technical barriers.
• Responsible AI Governance: embed privacy, security, compliance and oversight throughout.

Skills

Python

R

Java

Education

Master's or PhD in Computer Science, Data Science, or related field

Tools

TensorFlow

PyTorch

Scikit-learn

Job description

Key Responsibilities

• AI Opportunity Assessment Scoping Work with business stakeholders to understand business problems assess where AI can create value determine whether AI is the appropriate solution and translate opportunities into clearly defined use cases requirements scope success measures and implementation plans
• Technical Solution Design Define and review solution architecture technical requirements data requirements integrations infrastructure APIs security considerations and other dependencies required to implement AI solutions
• AI Development Implementation Develop configure integrate and or support the implementation of AI and data-driven solutions including Machine Learning Generative AI Large Language Models LLMs and AI agents
• Technical Feasibility Data Readiness Assess technical feasibility data availability and quality integration requirements infrastructure readiness technical dependencies risks and constraints before solutions progress into development or implementation
• AI Evaluation Testing Assurance Define appropriate technical evaluation frameworks test criteria benchmarks and acceptance thresholds to assess model and solution performance reliability robustness security safety bias and other relevant risks before deployment and throughout the solution lifecycle
• Proof of Concept Pilot Management Support the design and execution of PoCs and pilots including technical setup testing performance measurement issue resolution documentation and recommendations regarding whether solutions should progress be modified or discontinued
• Vendor Technology Evaluation Technically assess vendor solutions and proposals review architecture and technical claims challenge assumptions support build-versus-buy decisions identify risks and dependencies and ensure vendor solutions meet organizational requirements and standards
• Deployment Operationalization Work with relevant technical teams to transition successful solutions into production including integration deployment documentation monitoring support models and handover requirements
• Performance Monitoring Continuous Improvement Establish appropriate monitoring mechanisms and performance indicators for deployed AI solutions and support ongoing optimization retraining evaluation and lifecycle management
• Project Lifecycle Technical Support Maintain visibility of technical milestones dependencies risks issues decisions and deliverables across multiple AI initiatives and proactively support resolution of technical barriers to implementation
• Responsible AI Governance Technical Considerations Embed technical requirements related to AI governance data privacy cybersecurity responsible AI regulatory compliance human oversight traceability and risk management throughout the AI project lifecycle

A Master's or PhD in Computer Science, Data Science, or a related field, showcasing a strong academic foundation in AI principles.

Proven experience of 3+ years in developing and deploying machine learning models in a professional setting.

Familiarity with programming languages such as Python, R, or Java, demonstrating the ability to write efficient and maintainable code.

Hands-on experience with machine learning frameworks like TensorFlow, PyTorch, or Scikit-learn, indicating practical skills in model building.