Refactor, maintain, and scale the core backend and AI framework layers of the platform, eliminating technical debt to support multi-tenant growth.

Implement and monitor production LLM observability, focusing on token cost optimization, retrieval strategy tuning, and real-time model/data drift detection.

Build and manage automated prompt versioning systems, A/B testing pipelines, and LLM evaluation frameworks to ensure high response quality and consistency.

Build out next-generation copilot services and agentic workflows that autonomously produce structured communications (emails, text messages, and call scripts).

Deliver reliable Python backend services, integrations, and APIs that interact directly with vector databases (Pinecone) and LLM providers.

Drive features from concept to production, working directly within client cloud environments while maintaining strong code quality and operational standards.

• 5+ years of overall software development experience with a heavy focus on backend systems.
• 3+ years of production-level engineering experience with Python.
• 2+ years of hands-on experience building, deploying, and maintaining Retrieval-Augmented Generation (RAG) systems in live production environments.
• 1+ years of hands-on experience integrating and utilizing LLM observability and tracing tools (e.g., LangFuse, LangSmith, or equivalent platforms).
• 1+ years of experience establishing prompt versioning, A/B testing, and evaluation frameworks for LLMs.
• 1+ years of experience implementing data drift and model drift monitoring for production AI workloads.
• Track record of operating as a true senior engineer—taking direct ownership of deliverables, resolving ambiguity, and driving technical execution without waiting for step-by-step instructions.
• Fluent English (spoken and written) for daily technical collaboration with US-based teams.