Artificial Intelligence Architect (Senior or Lead)

Boeing•Long Beach, CA

2d•$142,800 - $249,550•Hybrid

About The Position

The Boeing Company is seeking an Artificial Intelligence Architect (Senior or Lead) to support their Enterprise Services Technology and Systems Data Management Office team. Boeing is the world's largest aerospace company and leading manufacturer of commercial airplanes and defense, space and security systems. This position sits within Boeing’s Enterprise Services organization, which provides strategic partnership and support to all Boeing business units. The individual in this role would be a part of Enterprise Services Technology and Systems Data Management team and is expected to be curious, look for insights in data that can turn into actionable and measurable outcomes, and be a storyteller at their core.

Requirements

5+ years of experience Data Engineering and/or Software Development
5+ years of experience with Artificial Intelligence (AI) hardware and software integration
5+ years of enterprise architecture experience, including but not limited to cloud architecture, security, data privacy, integration, and deployment
5+ years of experience developing information architecture and working with databases
5+ years of experience with Cloud hosted platforms such as AWS, Google Cloud Platform, or Azure
3+ years of experience with Artificial Intelligence (AI) and Machine Learning (ML) technologies, including the ability to integrate AI-driven insights into data architecture and analytics processes
3+ years of experience with modern programming and scripting languages such as C++, Java, JavaScript, Perl, Python, Visual Basic
3+ years of experience researching and applying large language and generative AI models
Experience training machine learning models in a cloud computing environment such as AWS, Google Cloud Platform, Microsoft Azure, etc. and fundamental knowledge in Machine Learning Operations (MLOps)

Nice To Haves

10+ years of related work experience or an equivalent combination of education and experience
Bachelor’s of Science degree or higher from an accredited course of study in data science, machine learning, applied statistics, mathematics, computer science or engineering
Experience working with or building on top of visual automation and conversational platforms (e.g., Microsoft Copilot Studio, Microsoft Power Platform, AWS Kiro, or similar enterprise low-code tools).
Experience with problem-solving skills and attention to detail
Experience with interpersonal and communication skills
Experience with a continuous improvement mindset
Experience in taking problems, creating solutions, and executing to deliver value for customers
Experience mentoring and coaching employees
Experience delivering business and data analytics solutions in a manufacturing, engineering, energy sector, oil and gas industry, or another type of operations environment is a plus
Experience in collaborating across the globe
Willing and able to travel up to 20% domestically

Responsibilities

Designs end-to-end Artificial Intelligence (AI) solutions, including data ingestion, model training/fine-tuning, deployment, monitoring, lifecycle management, and machine learning architectures
Leads the development of AI initiatives from Proof of Concepts (PoCs) to scalable, enterprise-wide production deployments
Evaluates and recommends AI technologies, including Deep Learning, Natural Language Processing (NLP), Computer Vision, and Generative AI, based on business use cases, cost, latency, and data privacy considerations
Leads design of AI architectures, Large Language Model/Retrieval-Augmented Generation (LLM/RAG) pipelines, and agentic workflow orchestration aligned to business use cases
Designs and builds low-code and no-code abstraction layers, visual workflow nodes, and template engines for agent creation
Defines AI strategy, roadmaps, and technology standards by evaluating emerging Artificial Intelligence/ Machine Learning (AI/ML), agentic framework, automation technologies, and makes recommendations for adoption within the business
Balances hands-on AI model development with cross-functional coordination, ensuring knowledge-based AI adoption through robust compliance, monitoring, and integration practices
Builds functional PoCs and minimum viable products (MVPs) to validate architectural assumptions and technical viability