Position: Senior AI Engineer

Work Location: Abu Dhabi

Experience: 8+ years in AI/ML

Employment Type: Full time and Remote

• Advanced Degree: Master's or Ph.D. in Computer Science, Data Science, AI, ML, or a related quantitative field.
• Proven Experience: 8+ years in AI/ML, including 3+ years designing, deploying, and maintaining production ML systems.
• Leadership: 2+ years in a leadership/senior technical role with team building and mentoring experience.
• Delivery Track Record: Demonstrated success in taking AI-powered products/features from concept to production deployment.
• Core Technical Expertise: Deep proficiency in Python, TensorFlow/PyTorch, Hugging Face, and cloud platforms (AWS/Azure/GCP).
• Cutting-Edge AI: Hands-on experience with LLMs (deployment/optimization), Generative AI applications, vector databases, and RAG architectures. (Also implies CV/NLP).
• MLOps Mastery: Experience with MLOps tools (MLflow, Kubeflow, Airflow) and CI/CD for ML model lifecycle management.
• Algorithmic Knowledge: Strong foundation in supervised, unsupervised, and reinforcement learning techniques.
• Scalable Systems: Experience with distributed computing frameworks (Spark, Ray).
• Responsible AI: Strong understanding of data privacy, AI ethics, model fairness, explainability, and compliance.
• Problem Solving: Ability to translate complex business problems into actionable AI solutions and insights.
• Domain Knowledge: Familiarity with [Target Industry - e.g., healthcare, fintech, government, smart cities].

Roles and responsibilities:

• Lead Development: Design and build scalable production AI/ML models/pipelines (LLMs, CV, NLP, GenAI).
• Solve Business Problems: Translate complex business challenges into actionable AI solutions and insights.
• Drive Product Integration: Embed AI capabilities into product roadmaps via cross-functional collaboration (Product/Engineering/Business).
• Evaluate Emerging Tech: Assess and integrate cutting-edge AI technologies (e.g., LLMs, GenAI, CV, NLP).
• Build & Lead Teams: Recruit, manage, and mentor a high-performing AI/ML engineering team.
• Foster Innovation: Cultivate a culture of experimentation and technical excellence.
• Ensure Responsible AI: Implement model governance, fairness, explainability, and compliance standards.
• Implement MLOps: Establish CI/CD pipelines, model monitoring, and MLOps best practices.
• Deliver End-to-End: Develop POCs, MVPs, and deploy production-grade models using leading frameworks.
• Champion Best Practices: Define and enforce AI/ML engineering standards and architectural principles.
• Own Technical Strategy: Guide architectural decisions for robust, scalable AI systems.
• Promote Experimentation: Encourage continuous exploration and adoption of innovative AI approaches.