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.

JOB DUTIES AND 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.

QUALIFICATIONS:

Knowledge, Skills, and Abilities

• Technical Skills:
  • 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
• Preferred:
  • 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

Education and Experience

• Required:
  • 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
• Preferred:
  • 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

Disclaimer

This job description has been designed to indicate the general nature and level of work performed by employees within this classification. It is not to be construed as a comprehensive inventory of all duties, responsibilities and qualifications required of employees assigned to this job. A-dec reserves the right to assign job duties and days and hours of work based on factors such as workload requirements, staffing levels, and customer demands.