AI Engineer, Principal

A-dec Inc.•Newberg, OR

About The Position

At A-dec, we do more than create the highest quality products and services for the dental industry; we strive to deliver a superior employment experience for each of our team members. With an environment that encourages and assists each person in developing to their highest potential, a career at A-dec is incredibly rewarding. Overview 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. 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.

Requirements

Expert level proficiency in Python for building production AI systems, services, and automation
Deep understanding of data architecture, feature engineering, and data quality at enterprise scale
Strong systems design skills spanning AI models, APIs, data platforms, and enterprise integrations
Expertise in cloud-based AI architectures and deployment patterns across Azure, AWS, or GCP
Strong grounding in software engineering best practices including version control, CI CD, testing, and documentation
Expert knowledge of security, privacy, and responsible AI considerations in regulated business environments
Ability to influence technical direction without direct authority
Bachelor’s degree in computer science, engineering, data science, mathematics, or related field, or equivalent practical experience
Fifteen plus years of progressive engineering experience with significant time spent building and scaling production AI systems
Demonstrated experience leading the architecture of complex, enterprise wide AI initiatives
Hands-on experience designing AI platforms rather than single use solutions
Deep practical experience using large language models in production business environments
Experience leading vendor evaluations, proofs of concept, and strategic technology selections
Proven track record of mentoring senior engineers and shaping applied AI practices across teams

Nice To Haves

Strong applied evaluation skills including experimentation design, prompt and model testing, A B testing, and production monitoring
Deep understanding of cost optimization strategies for LLMs and agentic systems including model selection, inference efficiency, and caching
Strong familiarity with MLOps platforms, model governance, and lifecycle automation
Extensive hands-on experience with machine learning frameworks such as scikit learn, PyTorch, or TensorFlow
Advanced experience in integrating AI solutions with ERP, CRM, and workflow platforms
Experience with enterprise data platforms such as Snowflake, Microsoft Fabric, and Dynamics D365 integrations
Experience with workflow orchestration tools such as Airflow, dbt, or cloud native alternatives

Responsibilities

Define and own the enterprise AI technical architecture, standards, and reference patterns across models, data, platforms, and integrations
Lead the design and delivery of complex, cross functional AI systems including multi agent architectures, LLM based platforms, and decision intelligence solutions
Serve as the technical authority for applied AI, providing guidance on model selection, system design, scalability, performance, and cost optimization
Design and oversee reusable AI platforms, services, and frameworks that enable multiple teams to build and deploy AI capabilities consistently and safely
Architect and govern agentic AI systems including orchestration strategies, agent collaboration patterns, MCP enabled APIs, and secure tool and data access
Drive advanced Large Language Model implementations including retrieval augmented generation, fine tuning strategies, evaluation frameworks, and production guardrails
Establish enterprise best practices for AI lifecycle management including deployment, monitoring, drift detection, observability, and retirement
Partner with data, security, legal, and compliance teams to define responsible AI standards, auditability, and risk management practices
Evaluate and influence vendor strategy including third party AI platforms, tooling, and in house build versus buy decisions
Lead technical design reviews and provide architectural oversight across AI initiatives
Mentor senior engineers, data scientists, and product teams on applied AI design patterns and best practices
Translate complex AI concepts into clear recommendations for senior leaders and business stakeholders
Measure, continuously improve and communicate the business impact, reliability, and cost efficiency of AI solutions
Remain 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.