Job Description

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)?