AI Solution Engineer

Job description

Our Partner is a Saudi Arabian conglomerate that stands today as one of the Middle East’s most influential family businesses, blending commercial expansion, global partnerships, and impactful philanthropy. Over eight decades, it has diversified into seven major sectors, including automotive, energy and financial services, operating in over 30 countries.

Together, we are looking for a Senior AI Solution Engineer, who will help architect and implement AI-driven solutions that enhance automation, reasoning, and decision-making across business functions. You’ll bridge high-level AI architecture with hands-on implementation, integrating intelligent agents into CRM and enterprise platforms to deliver business outcomes at scale.

Responsibilities

• Design and develop autonomous software agents using state-of-the-art LLM frameworks and agent orchestration tools (e.g. LangChain, LangGraph, Semantic Kernel, CrewAI).

• Implement scalable, modular, and extensible AI systems using Python that support multiple departments.

• Collaborate with product owners and domain experts to build reusable components and agentic workflows for business process automation.

• Integrate agent systems with enterprise platforms, including Dynamics 365 and cloud-native data infrastructures (e.g., Databricks, Azure, AWS).

• Translate business requirements into end-to-end AI solutions using MLOps practices and tools like MLflow, Kubeflow, or SageMaker.

• Develop APIs, event-driven pipelines, and CI/CD automation for robust production deployment and monitoring of AI agents.

• Optimize agents for reasoning, speed, performance, and maintainability across a federated enterprise setup.

• Contribute to the stability and scalability of agent-based services with strong DevOps collaboration.

Requirements

• 5+ years of hands-on experience in applied AI/ML engineering.

• Proven track record deploying AI models and solutions in production environments.

• Strong software engineering background in Python with practical experience in LLMs, autonomous agents, and prompt engineering.

• Experience with agentic AI tools and frameworks (LangChain, Semantic Kernel, CrewAI, LangGraph).

• Deep understanding of MLOps and cloud-native deployment using Databricks, SageMaker, Kubeflow, or similar.

• Familiarity with cloud platforms (Azure or AWS) and enterprise data pipelines/lakehouse architectures.

• Experience in API development, event-driven architectures, and enterprise application integration.

• Fluent in English with strong communication and problem-solving skills.