Principal AI Engineer
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Summary
Early engineering hire at Obin, a startup building LLM-powered multi-agent systems to automate credit workflows. The engineer designs agentic AI architectures (LLM, RAG, orchestration), owns production backend/API infrastructure, evaluates and optimizes for accuracy, latency, cost and safety, and works directly with credit-firm customers. Core stack: Python, LLM/agent frameworks, GCP, APIs.
The Role:
We're looking for AI Engineers who can help us build and scale intelligent agents from the ground up. You’ll join our team as one of our earliest hires. You'll work directly with the founders (ex-Google) and own key technical decisions from day one.
You’ll be
● Owning agentic AI strategy across the company's product suite; sets governance standards for agent evaluation, safety and autonomy.
- Recognized technical authority in multi-agent systems; publishes reusable blueprints/reference architectures; represents the company externally (conferences, open source, technical publications).
- 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:
- 10-15+ years of software engineering experience
- 5-8+ years of hands-on experience building AI/ML systems/frameworks , LLM-based agents and/or RAG systems in production
- Logging, evaluating, optimizing AI applications
- Sets architecture for agentic AI systems across products; solves ambiguous problems inherent to non-deterministic systems (context pollution, runtime state handoffs, safety).
- Expert in agent-to-agent (A2A) orchestration protocols, self-correcting agent loops, prompt-routing middleware; drives AI safety/reliability practices and evaluation standards; mentors senior engineers.
- 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