Principal AI/ML Solutions

Job Title

Principal of AI/ML Solutions

Position

Business Unit: Al Ghurair Investment

Reports To: SVP of Artificial Intelligence

Department

Group IT

Purpose

Al Ghurair Investment (AGI) is seeking a highly skilled, hands‑on AI/ML Expert who can independently design, build, and implement advanced AI solutions—covering Machine Learning, Generative AI, and agentic AI systems. This role requires deep technical ownership, where you personally drive end-to-end solution development, from architecture and experimentation to deployment and optimization. You will define and execute the architecture for scalable, secure, and high-performance AI platforms, with strong emphasis on transformer-based models, LLMs, and cutting‑edge generative technologies. While you will work independently on core technical development, you will also collaborate with data scientists, ML engineers, and business stakeholders to ensure the solutions seamlessly integrate into the broader enterprise ecosystem and deliver measurable business impac

Key Accountabilities Business Partnering & Opportunity Scouting: Act as the primary AI strategic partner for business unit leaders. Proactively scout operational bottlenecks, understand commercial drivers, and translate business friction into viable, high-ROI AI and agentic use cases. Rapid Prototyping & Iteration: Lead rapid experimentation and Proof‑of‑Concept (PoC) cycles directly alongside business stakeholders. Validate commercial viability, user adoption, and technical feasibility swiftly before committing to full‑scale development. Vendor Orchestration & Technical Governance: Direct and govern external technology vendors and top‑tier consulting partners. Ensure all vendor‑delivered AI solutions and agentic workflows align with internal architectural blueprints, enforcing strict IP retention and technical quality. Agentic Architecture & Core Development: Independently design, build, and take full technical ownership of autonomous AI frameworks. Architect advanced cognitive components—including agent memory, reflection loops, vector stores, and long‑horizon planning mechanisms. Enterprise Integration & Tooling: Develop robust integration patterns connecting LLMs and agentic systems to the enterprise ecosystem. Establish the architectural standard for how intelligent agents interface with internal databases, APIs, and legacy platforms. LLMOps, Governance & Observability: Define and implement the operational frameworks for production AI. This includes continuous performance monitoring, robust security controls, and Responsible AI governance for all autonomous systems. Communication

Internal: 1 Group IT Team, 2 Business stakeholders, 3 Product Owners, Process Owners

External: 1 Regulatory Bodies, 2 Technology Vendors, 3 Implementation Partners

Knowledge and Experience Knowledge Education & Certifications: Master’s degree (preferred) or Bachelor’s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related technical field. Advanced certifications or specialization in AI/ML architecture, cloud platforms, or generative AI technologies are a plus. Experience & Skill Strategic AI Delivery: 8–10 years of experience in AI/ML solution design, with a proven track record of translating business friction into measurable, high‑ROI technical implementations. LLM & Agentic Architecture: Proven production experience deploying LLMs (e.g., GPT, Claude) and designing multi‑agent workflows within an enterprise setting. Vendor Technical Governance: Demonstrated experience directing and governing external technology vendors, ensuring strict adherence to internal architectural blueprints, code quality, and IP retention. Rapid Prototyping: Strong proficiency in Python and modern orchestration frameworks (e.g., LangChain, AutoGen, CrewAI) to personally lead rapid Proof‑of‑Concept (PoC) cycles before scaling via vendor teams. Enterprise Integration: Deep understanding of how to connect AI/ML systems to legacy enterprise platforms, including expertise in APIs, vector databases, and prompt engineering strategies. Cloud AI Architecture: Experience in cloud‑native AI architecture on Azure (including Azure ML/AI platform, Copilot Studio, and Azure Foundry) or equivalent enterprise cloud services. LLMOps & Governance: Experience defining operational frameworks, event‑driven architectures, and Responsible AI controls for production‑grade, autonomous systems. #J-18808-Ljbffr