Principal AI Engineer at Obin AI (New York, NY Metro)Principal AI Engineer

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

● 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