Agentic AI Engineer
Posted Updated 16
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The Role:
We're looking for AI Engineers who can help us build and scale intelligent agents from the ground up. You'll work directly with the founders (ex-Google) and own key technical decisions from day one.
You’ll be
● Designing and Deploying LLM-Powered Agents
Build agentic systems using LLMs and RAG to automate complex credit workflows. This includes prompt design, tool use, orchestration logic, and system evaluation.
● Working Directly with Customers
Partner with technical and non-technical stakeholders at major credit firms to understand workflow pain points and co-develop AI solutions that directly tie to ROI.
● Owning Production Infrastructure
Architect and maintain backend systems and APIs that connect to data sources and customer platforms. Ensure high availability, performance, and observability.
● Evaluating and Optimizing Systems
Balance accuracy, latency, reliability, cost, and explainability in our agentic systems. Lead rigorous experimentation and improvement cycles.
● Contributing to Core Platform Design
Define technical architecture, build reusable infrastructure for agent deployment, and shape the foundation of Obin’s AI engineering best practices.
● Setting Culture and Standards
Help define Obin’s engineering culture, hiring bar, and long-term technical roadmap
We believe great AI Engineers come from diverse backgrounds and are unified by deep curiosity, pragmatism, and engineering excellence.
Experience Required:
● 3-5+ years of software engineering experience
● 1+ years of hands-on experience building LLM-based agents and/or RAG systems in production
● Logging, evaluating, optimizing AI applications
● Proficiency in Python and experience with modern AI/ML frameworks and cloud infrastructure (GCP preferred)
● Experience with cloud infra (preferably GCP) and APIs
● Hunger to move fast, own outcomes, and build something enduring
● Strong intuition around system architecture, performance, and scaling
● Clear communication, especially in ambiguous, high-stakes problem spaces
Bonus:
● Experience with human-in-the-loop systems, feedback loops, and long-horizon agentic task execution
● Background in financial systems, risk modeling, or decision automation
● Familiarity with PydanticAI, LlamaIndex, Google ADK, Claude Agent SDK, or similar frameworks
● You’ve built or contributed to an internal AI platform or reusable LLM tooling