Principal AI Engineer

Job Description Redtech is helping our client with a Direct Hire search for a Principal Engineer - AI Location: Issaquah (Seattle), WA - selected candidate must live within 50 miles of their selected work location. (Relocation assistance available for eligible new hires located over 50 miles from the Hub location of hire and they move to within 50 miles of the hub location) Relocation assistance available for eligible hires Schedule - 100% onsite 5 days/week Able to support off-hours work as required, including weekends, holidays, and 24/7 on-call responsibilities on a rotational basisBackground check & Drug Test - requires successful completion Eligibility - We are not able to offer any type of work sponsorship at this time Compensation - Target Pay Range: $180,000- $250,000/year DOE (top of the range only considered for exceptional experience) Role is eligible for bonus and RSU's Benefits offering - based on eligibility: CLEINT offers a comprehensive package of benefits including paid time off, health benefits - medical/dental/vision/hearing aid/pharmacy/behavioral health/employee assistance, health care reimbursement account, dependent care assistance plan, commuter benefits, short-term disability and long-term disability insurance, AD&D insurance, life insurance, 401(k), stock purchase plan, financial wellness program, to eligible employees. Description The Principal AI Engineer is the lead Architect and hands-on builder of our unified AI Platform as a Service. This role is responsible for transforming raw foundation model capabilities into a scalable, multi-tenant reasoning stack that empowers the entire enterprise to build, deploy, and manage semantic discovery, conversational intelligence, and autonomous agents. This role will balance 40% hands-on systems development with 60% platform strategy, personally coding the core orchestration engines, standardized capability servers, and universal trust guardrails. The mission is to provide a central 'AI Operating System' for the company, ensuring that specialized agents across different business units can communicate via inter-agent protocols, access grounded knowledge layers securely, and execute autonomous tasks within a governed, high-performance agent runtime environment. ROLE Develops and implements an industry-leading, self-service AI platform. This platform will offer standardized blueprints for engineering teams to utilize, including "macro-agents" and "micro-tools." Develops and articulates the long-term, multi-year technical roadmap for the AI Platform, ensuring its capabilities are strategically aligned with the overarching business objectives. Develops and implements complex stategraphs to manage edge cases and enable self-correction in autonomous planning. Leads the architecture and hands-on development of remote MCP servers and implements custom function calling to securely connect agents with sensitive enterprise data. Defines and implements the communication standards for agent-to-agent interactions to facilitate autonomous discovery and task hand-offs between agents developed by various business units. Ensures the agent identity layer is architected for granular permissioning and non-repudiation, specifically regarding every autonomous system action. Develops a unified knowledge layer for the platform, leveraging semantic retrieval engine and multimodal grounding. This layer will serve as the single source of truth for all connected agents, providing "truth-as-a-service." Develops and implements a global memory bank architecture, leveraging semantic retrieval engine and graph databases. This system will be essential for preserving context and capturing "institutional knowledge" from interactions over time. Develops the platform's trust layer by automating rapid evaluation pipelines. These pipelines will measure key metrics of success, cost, and safety for agentic behavior across all tenants. Ensures the agent runtime environment lifecycle is managed to guarantee high availability, session persistence, and global scalability for the company's digital workforce. Performs in-depth, rigorous code reviews with a specific focus on identifying and mitigating the unique failure modes inherent in agentic systems, such as state bloat, tool call hallucinations, and infinite loops. Implements advanced techniques, such as prompt caching and model routing to optimize inference costs and latency. Serves as the Engineer's Engineer, providing mentorship to Senior and Staff Engineers in advanced techniques such as prompt engineering, model distillation, and agentic evaluation. Influences the roadmap for AI services by collaborating with Cloud and Infrastructure product teams to address enterprise-specific needs. Ensures the longevity, scalability, and quality of our systems through continuous improvement, comprehensive documentation, meticulous profiling, and significant performance enhancements. REQUIRED 10+ years in Software Engineering, with at least 4 years in a Principal or Architect-level role. 2+ years specifically architecting LLM-based systems, with a proven track record of moving agentic projects into production at scale. 5+ years of experience developing within an agile methodology. Certified Google Cloud Professional Cloud Architect. Experience leading technical workstreams, translating business problems into AI-native architectures. Expertise in asynchronous orchestration frameworks (e.g., Python) and proficiency in statically typed systems (e.g., Java, Go, or Rust) is required to engineer high-concurrency agentic middleware using stateful graph orchestration (e.g., ADK or LangGraph) to power robust, autonomous reasoning engines. Expertise in cloud-native CLI tools and Infrastructure-as-Code frameworks for automating agentic infrastructure deployment. Proven track record of deploying and scaling containerized autonomous workloads using enterprise-grade container orchestration and serverless execution platforms. Experience managing high-scale distributed architecture, vector databases, graph databases, and structured data pipelines. Deep knowledge of stateful orchestration frameworks and multi-agent design patterns, with the architectural expertise to engineer custom reasoning engines and proprietary orchestration logic when off-the-shelf solutions reach their scaling or safety limits. Practitioner understanding of Chain-of-Thought, ReAct, Tree-of-Thoughts, and Self-Reflection architectures. Experience managing systems with millions of daily requests or handling multi-petabyte datasets. Proficiency in architecting semantic retrieval layers, attribute-aware discovery, and stateful persistence systems to provide high-fidelity long-term context for autonomous agents. Deep understanding of MCP, A2A, REST/gRPC APIs, Oauth2 security, and function calling mechanics. Familiarity with design patterns and microservices-based architecture patterns. Mastery of distributed traceability, neural telemetry, and cognitive debugging suites to audit and visualize logic trajectories across complex inter-agent handoffs. Understanding of global AI regulations (e.g., EU AI Act) and how to translate them into technical guardrails. Strong verbal and written communication skills and be able to communicate to both technical and Business audiences. Ability to work under pressure in crisis with a strong sense of urgency. Responsible, conscientious, organized, self-motivated, and able to work with limited supervision. Detail-oriented and possess strong problem-solving skills and ability to analyze potential future issues. Able to support off-hours work as required, including weekends, holidays, and 24/7 on-call responsibilities on a rotational basis. Recommended Bachelor's or Master's in Computer Science or Artificial Intelligence. PhD in AI, Distributed Systems, or Cognitive Science. Certified Google Cloud Professional Machine Learning Engineer or any agentic AI specialty certification focusing on multi-agent Systems and autonomous reasoning. 3+ years distributed cache technologies Experience with deploying and configuring Cloud Platform resources. Experience working in a retail ecommerce environment. Proficient in Google Workspace applications, including Sheets, Docs, Slides, and Gmail.

Created: 2026-03-04