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AI Agent Engineer

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Location
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Work Model
home_work hybrid
Job Type
work Full-time
Salary
payments Undisclosed

Intelligence Match

Match: ??%

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Skills Match 87%
Experience Match 88%
Portfolio Match 84%
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About the role

oar is a global fintech startup that specializes in financing and investment. Currently headquartered in Saudi Arabia, Soar is growing throughout the region with a mission to help people achieve their financial goals with innovative financial and property investment solutions and tools through its multi-purpose platform, designed to offer a simple and seamless user experience. Role Summary: This role is for engineers who operate at a higher abstraction level. You will design, manage, and optimize AI agents that generate code, workflows, and business logic. Your responsibility is to ensure outputs are correct, scalable, and aligned with business goals. You are expected to: Reduce development time through AI Improve output quality through validation and iteration Continuously refine agent performance Key Responsibilities: Design and manage agent-based development workflows Oversee AI-generated code: validate, test, refactor, and productionize Build systems to evaluate agent output quality and reliability Fine-tune prompts, tools, and pipelines to improve outcomes Integrate AI agents into the software development lifecycle (SDLC) Translate business problems (mortgage, real estate workflows) into AI-driven solutions Establish best practices for multi-agent collaboration and orchestration Monitor performance, cost, and accuracy of AI systems Work across product and business teams to identify automation opportunities Requirements Hands-on experience with LLM-based tools and workflows (e.g., Anthropic Claude or similar) Experience building or managing AI-assisted or agent-driven systems 

Required Skills

Preferred Skills

Python Machine Learning Microservices Google Cloud (GCP)

Responsibilities

  • Design and manage agent-based development workflows
  • Oversee AI-generated code: validate, test, refactor, and productionize
  • Build systems to evaluate agent output quality and reliability
  • Fine-tune prompts, tools, and pipelines to improve outcomes
  • Integrate AI agents into the software development lifecycle (SDLC)
  • Translate business problems (mortgage, real estate workflows) into AI-driven solutions
  • Establish best practices for multi-agent collaboration and orchestration
  • Monitor performance, cost, and accuracy of AI systems
  • Work across product and business teams to identify automation opportunities
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