Senior AI Engineer / AI Architect

Responsibilities

• Design, build, and deploy scalable, robust, and high-impact AI solutions specifically Agentic AI, RAG, GraphRAG, LLM Multimodal Chatbot Solutions , from proof-of-concept to production.
• Mentor and collaborate with other engineers and data teams, helping to establish a culture of technical excellence and innovation.
• Architect and manage AI/ML infrastructure using cloud services (preferably Azure or On-Prem Openshift) and container orchestration platforms like Kubernetes.
• Optimize and scale model deployment, including implementing efficient GPU inferencing pipelines for low-latency, high-throughput applications.
• Establish rigorous frameworks for model evaluation (Evals), validation, and monitoring, ensuring model explainability, fairness, and transparency.
• Champion a modern, collaborative AI development lifecycle; leverage AI coding assistants (e.g., Cursor, Claude Code) to translate detailed Product Requirement Document (PRD) specifications into high-quality code, and enforce a strict PR-based workflow with automated testing for all code contributions.
• Drive the exploration and implementation of Knowledge Graphs (e.g., TigerGraph, Neo4j) and LLMs to model complex biomedical data and power intelligent systems.
• Develop and apply Reinforcement Learning (RL) models to optimize processes within Clinical Decision Support Systems.
• Collaborate with cross-functional teams, including clinicians and product managers, to ensure our AI solutions meet critical needs and integrate seamlessly into clinical workflows using standards like FHIR/HL7.

Technical & Functional Skills:

• Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or a related technical field.
• 6+ years of experience in AI, machine learning, or data science, with a proven track record of delivering high-impact solutions. Leadership or mentorship experience is highly valued.
• Hands-on experience deploying and scaling machine learning models in production using Kubernetes, with a focus on performance and reliability.
• Experience with optimizing model serving, including GPU inferencing and framework-specific performance tuning.
• Deep understanding of model evaluation techniques, A/B testing, and AI explainability methods (e.g., SHAP, LIME).
• Proficiency or experience with AI-assisted development tools (e.g., Cursor, GitHub Copilot, Claude Code).
• Experience with automated testing frameworks (e.g., pytest, unittest, pydantic) and CI/CD practices for machine learning.
• Expertise in designing or utilizing Knowledge Graphs, with experience in graph databases such as TigerGraph or Neo4j.
• Familiarity with Reinforcement Learning (RL) concepts and their practical application.
• Knowledge of healthcare data standards (FHIR/HL7) is a significant plus.
• Proficiency in Python and common ML/Data Science libraries (e.g., scikit-learn, pandas, PyTorch, TensorFlow).
• Experience with LLMs, NLP techniques, and agentic frameworks (e.g., LangChain, CrewAI, Microsoft Agentic Framework, MCP, A2A ).
• Strong experience with cloud platforms (AWS, Azure, or GCP).
• Excellent communication skills and a collaborative, team-oriented mindset. Critical Skills
• Customer Focused: A passionate drive to delight end users with high quality / scalable solutions.
• Critical Thinking: A thoughtful process of analyzing complex data to reach well-reasoned, effective solutions.
• Team Mentality: Partnering effectively to drive our culture and execute on our common goals.
• Business Acumen: An appreciation and understanding of the healthcare or financial services industry to make sound decisions.
• Learning Agility: An openness to new ways of thinking and acquiring new skills to retain a competitive advantage

Job Type: Contract
Contract length: 6 months

Application Question(s):

• Do you have at least 6 years of experience in Artificial Intelligence, Machine Learning, or Data Science?
• Do you have hands-on experience in deploying and scaling ML models using Kubernetes or other container orchestration platforms? Please specify which.
• Have you worked with cloud platforms such as Azure, AWS, or GCP? If yes, please mention which and for how long.
• Describe your experience in optimizing GPU inferencing pipelines for AI/ML applications.
• Do you have experience applying Reinforcement Learning (RL) in real-world solutions? If yes, please provide an example.
• What is your current location and availability to join if selected?
• Have you worked on AI solutions in healthcare or clinical domains, especially involving FHIR/HL7 standards?
• What are your salary (AED) expectations (monthly)?
• What is your nationality?
• Do you agree to work on a 6-month (extendable) contract basis?
• Do you hold a valid work permit or visa to work in the job location (e.g., Riyadh, Saudi Arabia)?