AI Solutions Engineer

Key Responsibilities:

AI Use Case Implementation

Translate complex business needs into AI/ML-based technical solutions.

Design, build, and deploy intelligent agents such as chatbots, summarizers, and document automation tools.

Collaborate with cross-functional teams (data engineers, analysts, etc.) for model training and fine-tuning.

End-to-End Project Execution

Lead full-cycle development of AI solutions from data ingestion to deployment.

Implement data preprocessing, feature engineering, and model validation workflows.

Develop scalable, production-ready GenAI models and integration pipelines.

Build and optimize GenAI workflows using APIs, Azure AI tools, and Copilot Studio.

Collaboration & Documentation

Liaise with stakeholders to gather, clarify, and refine AI requirements.

Maintain documentation of models, technical architecture, and delivery milestones.

Prepare demonstrations, presentations, and user documentation for AI tools.

Monitoring & Continuous Improvement

Track performance of deployed models and ensure consistent accuracy.

Integrate feedback loops and retrain models as necessary.

Apply MLOps practices for model versioning, monitoring, and scaling.

Qualifications & Skills:

2–5 years of experience in developing and deploying AI/ML solutions in production.

Proven experience in AI/ML model lifecycle, including data prep, training, tuning, and deployment.

Hands-on experience with Azure AI Foundry, Copilot Studio, and GenAI implementation.

Strong programming skills in Python and libraries like scikit-learn, PyTorch, TensorFlow, LangChain, etc.

Familiarity with data processing frameworks: Pandas, Spark, SQL (preferred).

Knowledge of MLOps practices and model governance in production environments.

Strong communication skills and ability to work collaboratively with business and technical teams.

Arabic language proficiency is a strong advantage due to business requirements