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Remote role but specialized production GenAI requirements reduce broad applicant pool.
Highly domain-specific GenAI production expertise required, limiting cross-industry transferability.
Non-negotiable production GenAI experience, specific tooling, and leadership needs drive high shortlisting strictness.
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Own the architecture underpinning the shared AI pipeline for multiple GenAI B2SMB products, including retrieval, agent reasoning, state management, failure handling, and model orchestration.
Design and maintain reusable AI system patterns, prompts, evaluation frameworks, and fallback model strategies ensuring production readiness and quality at scale.
Mentor engineers on AI system designs, communicate trade-offs clearly, and deliver measurable, reliable AI capabilities, focusing exclusively on AI system architecture (not research or full DevOps).
5+ years in engineering, AI, ML, or data-product roles, with 3+ years hands-on experience in production GenAI/LLM systems handling real traffic.
Deep expertise in LLMs and SLMs including prompt engineering, structured outputs, tool and function calling.
Hands-on experience with multiple major LLM ecosystems and RAG pipelines using vector stores and retrieval evaluation.
Strong Python skills, familiarity with REST APIs, Docker, cloud platforms, CI/CD, and experience defining and applying evaluation metrics for AI outputs.
Experienced individual contributor with proven track record owning and scaling GenAI architectures in production environments, not limited to demos or research.
Comfortable designing agentic AI workflows that integrate memory, tool use, and safe failure modes balancing model decisions with deterministic logic.
Able to operate in a fast-paced environment creating structure amid shifting requirements, guiding engineers while maintaining hands-on technical involvement.