Applied AI Engineer

Applied AI Engineer Location: Bellevue, WA (Seattle area preferred)Compensation: $150,000-$300,000 DOE + equityTeam Size: 6 About the Company Our client is a well-funded, early-stage startup building an AI-first enterprise automation platform where intelligent agents resolve operational tasks autonomously, learn from outcomes, and continuously improve over time.This is an opportunity to join as an early Applied AI Engineer and own a significant portion of the agentic AI stack from day one. You'll help shape both the technical direction and engineering culture while working alongside a highly experienced founding team. The Role You'll design and build the core AI agent framework powering an autonomous enterprise execution platform. This role goes beyond prompt engineering. You'll architect orchestration systems, memory layers, evaluation frameworks, and agent pipelines from the ground up.Working closely with company leadership, you'll have significant influence on product strategy, technical architecture, and future hiring decisions. What You'll Own Design and build multi-agent orchestration systems that detect, route, delegate, and resolve complex workflows autonomously Implement episodic and semantic memory architectures, including vector databases, retrieval pipelines, and continuous learning mechanisms Build action libraries and third-party integrations across enterprise applications, identity systems, SaaS platforms, and knowledge repositories Develop LLM evaluation frameworks including prompt versioning, regression testing, and model benchmarking Architect human-in-the-loop workflows with structured outputs, validation layers, retry logic, and graceful escalation paths Instrument agent performance metrics, tracing, latency monitoring, and resolution analytics Collaborate on knowledge graph, memory, and learning-loop architecture as the platform evolves Required Qualifications 3+ years building production AI/ML systems serving real users and real traffic Hands-on experience designing multi-step LLM applications with tool use, structured outputs, and workflow orchestration Strong understanding of RAG architectures, chunking strategies, embeddings, hybrid retrieval, and re-ranking techniques Proficiency in Java, Python, asynchronous programming patterns, REST APIs, and schema validation frameworks Experience with evaluation-driven development and testing methodologies for AI systems Ability to thrive in a fast-paced startup environment with high ownership and ambiguity Nice to Have Experience with IT service management, enterprise SaaS platforms, or operational workflow automation Familiarity with AI governance, guardrails, and agent oversight frameworks Experience building agent memory systems or long-horizon autonomous task execution Knowledge of LLM observability and tracing tools Familiarity with open-source foundation models and model selection strategies