3643 Principal AI Engineer

Principal AI Engineer The Principal AI Engineer serves as the organization's most senior applied artificial intelligence (AI) technical expert, responsible for defining, architecting, and scaling enterprise grade AI capabilities across the business. This role sets technical direction for applied AI, agentic systems, and advanced analytics, ensuring solutions are secure, scalable, and aligned with long term business strategy. We value and encourage diversity in the workplace & women, minorities, and veterans are highly encouraged to apply. Thank you! More about this role: This role combines deep hands-on expertise with architectural ownership, technical governance , and mentorship. The Principal AI Engineer leads the design of complex AI systems that span multiple business domains including supply chain, finance, operations, sales, and customer service. Success is measured by sustained business impact, platform reuse, cost efficiency, and the ability to enable other teams to safely and effectively build on AI foundations. The role emphasizes production excellence, responsible AI practices, and pragmatic innovation rather than academic research.Type: Direct HireLocation: Portland, OR metro Job Duties and Responsibilities: Define and own the enterprise AI technical architecture, standards, and reference patterns across models, data, platforms, and integrationsLead the design and delivery of complex, cross functional AI systems including multi agent architectures, LLM based platforms, and decision intelligence solutionsServe as the technical authority for applied AI, providing guidance on model selection, system design, scalability, performance, and cost optimizationDesign and oversee reusable AI platforms, services, and frameworks that enable multiple teams to build and deploy AI capabilities consistently and safelyArchitect and govern agentic AI systems including orchestration strategies, agent collaboration patterns, MCP enabled APIs, and secure tool and data accessDrive advanced Large Language Model implementations including retrieval augmented generation, fine tuning strategies, evaluation frameworks, and production guardrailsEstablish enterprise best practices for AI lifecycle management including deployment, monitoring, drift detection, observability, and retirementPartner with data, security, legal, and compliance teams to define responsible AI standards, auditability, and risk management practicesEvaluate and influence vendor strategy including third party AI platforms, tooling, and in house build versus buy decisionsLead technical design reviews and provide architectural oversight across AI initiativesMentor senior engineers, data scientists, and product teams on applied AI design patterns and best practicesTranslate complex AI concepts into clear recommendations for senior leaders and business stakeholdersMeasure, continuously improve and communicate the business impact, reliability, and cost efficiency of AI solutionsRemain current with evolving AI trends by actively monitoring leading research and vendor roadmaps, prototyping emerging models and agent frameworks, and translating validated advancements into strategic recommendations and pilots that align with business priorities and deliver measurable value.Qualifications:Technical Skills:Expert level proficiency in Python for building production AI systems, services, and automationDeep understanding of data architecture, feature engineering, and data quality at enterprise scaleStrong systems design skills spanning AI models, APIs, data platforms, and enterprise integrationsExpertise in cloud-based AI architectures and deployment patterns across Azure, AWS, or GCPStrong grounding in software engineering best practices including version control, CI CD, testing, and documentationExpert knowledge of security, privacy, and responsible AI considerations in regulated business environmentsAbility to influence technical direction without direct authorityPreferred:Strong applied evaluation skills including experimentation design, prompt and model testing, A B testing, and production monitoringDeep understanding of cost optimization strategies for LLMs and agentic systems including model selection, inference efficiency, and cachingStrong familiarity with MLOps platforms, model governance, and lifecycle automationEducation and ExperienceRequired:Bachelor's degree in computer science, engineering, data science, mathematics, or related field, or equivalent practical experience15+ years of progressive engineering experience, with recent time spent building and scaling production AI systemsDemonstrated experience leading the architecture of complex, enterprise wide AI initiativesHands-on experience designing AI platforms rather than single use solutionsDeep practical experience using large language models in production business environmentsExperience leading vendor evaluations, proofs of concept, and strategic technology selectionsProven track record of mentoring senior engineers and shaping applied AI practices across teamsPreferred:Extensive hands-on experience with machine learning frameworks such as scikit learn, PyTorch, or TensorFlowAdvanced experience in integrating AI solutions with ERP, CRM, and workflow platformsExperience with enterprise data platforms such as Snowflake, Microsoft Fabric, and Dynamics D365 integrationsExperience with workflow orchestration tools such as Airflow, dbt, or cloud native alternatives

Created: 2026-04-02