AI Agent Software Engineer

Cooperidge Consulting Firm•San Francisco, CA

About The Position

Cooperidge Consulting Firm is seeking an AI Agent Software Engineer for a high-momentum AI platform leader in San Francisco, CA . This is a full-cycle ownership role for engineers who want to move beyond "opaque prompts" and build production-ready, autonomous agents. You will design AI systems that handle high-sensitivity tasks—such as password resets and complex account resolutions—traditionally managed by human teams in finance and healthcare. Utilizing a proprietary logic framework and real-time observability tools, you will build agents that are transparent, auditable, and capable of end-to-end issue resolution. This is an elite opportunity for a systems-minded engineer to join an engineer-led culture where "no prior AI experience" is required—only a mastery of robust software engineering and deep curiosity.

Requirements

Minimum of 2+ years of professional experience building production-grade software systems for real users.
Language Mastery: Strong proficiency in Python and TypeScript , with expertise in asynchronous programming and error handling.
Systems Expert: Excellent debugging skills using profilers, log aggregators, and raw data analysis to troubleshoot complex distributed systems.
Computer Science Foundation: Strong background in CS fundamentals; while specific language experience is flexible, a deep understanding of performance optimization is required.
The "AI Apprentice" Mindset: No prior AI experience is required; the team will provide comprehensive training on agentic reasoning and LLM integration.
Proactive Reasoning: A deep curiosity about how AI agents reason, why they fail, and how to make them more reliable.
Obsession with UX: A focus on building robust, transparent systems that solve real human problems.

Nice To Haves

Exposure to multimodal AI or voice interface development.
Experience with conversational automation frameworks or enterprise CX platforms.

Responsibilities

Autonomous Agent Design: Build AI-driven voice and chat agents that anticipate customer intent and resolve complex workflows end-to-end.
Logic Framework Implementation: Translate natural language instructions into structured, modular, and auditable workflows using a proprietary agent logic framework.
Observability & Tracing: Use real-time tools to trace agent decisions, ensuring every action is understandable, debuggable, and continuously improving.
Performance Engineering: Analyze large-scale performance data to identify behavioral trends and drive platform-wide improvements in agent reasoning.
Model Integration: Experiment with and tune the latest voice and language models for enterprise-grade reliability, low latency, and broad language coverage.
Full-Cycle Ownership: Take agent concepts from initial design and prototyping through to production deployment and rapid iteration.
Cross-Functional Partnership: Collaborate with product and operations teams to identify friction points and deliver measurable improvements to the customer experience.