AI Systems Engineer (Agentic Development)

About us: Soar 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 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 Strong understanding of modern SDLC with AI acceleration Experience with Google Cloud Platform (GCP) or similar cloud environments Ability to review and validate generated code across multiple languages Experience with prompt engineering, evaluation loops, and model tuning Strong system thinking: can design workflows, not just write functions Preferred Experience Experience with multi-agent systems and orchestration frameworks Familiarity with real estate or mortgage workflows (not required but valuable) Experience with vector databases, RAG pipelines, or knowledge systems Exposure to tools like “antigravity”-style AI workflows or similar emerging platforms What Success Looks Like Development cycles reduced significantly through AI leverage High-quality outputs from agents with minimal rework Clear frameworks for agent validation and monitoring Engineers spend more time on problem-solving and innovation, less on manual coding. Build an AI-first company from the ground up Work on real-world, high-impact problems in real estate and mortgage Operate in a fast, experimental, innovation-driven environment Shape how engineering is done—not just what is built