Job Description & How to Apply Below
The Machine Learning Engineer will be responsible for the following:
Machine Learning and AI Development- Translate business requirements and operational problems into clearly defined machine learning and AI use cases.
- Define measurable success criteria, evaluation methods and practical implementation approaches for AI solutions.
- Prepare, validate and transform datasets for model development and evaluation.
- Develop data-processing and machine-learning pipelines.
- Build, train, fine-tune and evaluate machine learning models using appropriate statistical and machine-learning techniques.
- Develop generative AI and large language model applications.
- Build retrieval-augmented generation (RAG), semantic-search and enterprise knowledge solutions.
- Develop intelligent agents that interact securely with tools, APIs, enterprise applications and other systems.
- Develop document-understanding, information-extraction and AI-assisted document-processing solutions.
- Select and benchmark models based on quality, accuracy, response time, infrastructure requirements, privacy, operating cost and maintainability.
- Build repeatable evaluation frameworks for model quality, retrieval performance, grounded responses, hallucination risk, tool usage and agent behaviour.
- Support proof-of-concept, proof-of-value, MVP and pilot implementations.
- Rapidly prototype AI capabilities for customer validation and evolve successful prototypes into maintainable production solutions.
- Work with software, infrastructure and Dev Ops teams to deploy AI solutions across cloud, private-cloud and on-premises environments.
- Monitor deployed models and AI services and support continuous performance improvement.
- Apply appropriate model versioning, experiment tracking, logging and AI-observability practices.
- Investigate model failures, data-quality issues and unexpected system behaviour and recommend corrective actions.
- Support model optimisation, inference-performance improvement and efficient infrastructure utilisation where required.
- Design AI solutions that operate within enterprise, government, data-sovereignty and restricted-network requirements.
- Apply appropriate controls for data privacy, access management and secure use of enterprise information.
- Integrate AI models and services through secure APIs and enterprise systems.
- Participate in technical discovery sessions, solution workshops, demonstrations and customer meetings where AI expertise is required.
- Support technical feasibility assessments for proposed AI use cases.
- Prepare technical documentation, evaluation reports and solution documentation.
- Clearly communicate assumptions, limitations, technical risks and dependencies to technical and non-technical stakeholders.
- Work closely with product, business, software-development and infrastructure teams throughout the solution lifecycle.
Any other responsibilities assigned by management as required.
Qualifications and Experience- Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science, Engineering, Mathematics or a related field.
- 3–5 years of relevant professional experience in machine learning, artificial intelligence, data science or a related technical role.
- At least 2 years of hands-on experience developing, deploying or evaluating machine learning, generative AI or LLM-based solutions.
- Practical experience developing machine-learning solutions beyond experimental notebooks.
- Strong practical experience with Python.
- Experience with machine-learning frameworks such as PyTorch, Tensor Flow or scikit-learn.
- Hands-on experience with LLMs, embeddings, vector search, RAG and tool-calling or agent workflows.
- Experience integrating models and AI services through APIs.
- Experience working with SQL, Git, Docker and software deployment pipelines.
- Experience deploying AI solutions into production or customer-facing environments is strongly preferred.
- Experience with private model hosting, on-premises AI or restricted enterprise environments is an advantage.
- Experience in Arabic or multilingual NLP, document AI or OCR is an advantage.
- Experience with Model Context Protocol (MCP), agent orchestration frameworks, MLOps or AI observability is an advantage.
- Experience delivering AI solutions for government or regulated organisations is preferred.
- Equivalent demonstrated technical capability and relevant practical experience may be considered in place of the stated number of years.
- Strong Python and…
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