Senior Machine Learning Architect

Senior Machine Learning Architect

Company:

The Boeing Company

The Boeing Company is currently seeking a Senior Machine Learning Architect to join the team in Seattle, WA; Arlington, VA; Auburn, WA; Berkeley, MO; Chicago, IL; Colorado Springs, CO; El Segundo, CA; Englewood, CO; Everett, WA; Hazelwood, MO; Houston, TX; Huntington Beach, CA; Huntsville, AL; Kent, WA; Long Beach, CA; Mesa, AZ; Miami, FL; North Charleston, SC; Plano, TX; Portland, OR; Renton, WA; Ridley Park, PA; Saint Charles, MO; San Antonio, TX; Seal Beach, CA; Titusville, FL; or Tukwila, WA. 

We are looking for a talented and experienced Senior Machine Learning Architecture leader to join our dynamic team. The ideal candidate will play a pivotal role in leading, designing and implementing scalable machine learning systems and architectures that empower our organization to make informed, data-driven decisions. As a Machine Learning Architecture leader, you will collaborate closely with cross-functional teams to thoroughly understand business requirements and translate them into high reliable solution designs. You will then lead highly scalable, secure, and cost-effective solutions tailored to address specific business challenges. This role demands a deep understanding of cloud, data and machine learning technologies, along with a proven track record in developing repeatable and reusable architecture frameworks. Join us in shaping the future of our cloud and data strategy!

Position Responsibilities:

• Define the strategy to build highly reliable and scalable ML and AI solutions that align with the organization’s business goals and objectives

• Lead the creation and implementation of scalable, robust, and high-performance ML architectures including MLOps, AIOps leveraging cloud native services (AWS, Azure, GCP) and open-source frameworks

• Design, build, and optimize machine learning models, ensuring accuracy, efficiency, and scalability

• Collaborate with data engineers, data scientists, software developers, and DevOps teams to integrate ML models into production systems

• Assess and recommend ML tools, frameworks, and platforms to deliver business value and foster innovation

• Monitor and optimize ML models and systems for latency, throughput, and cost-efficiency in production

• Provide technical guidance to ML engineers and data scientists including documenting standards and best practices

• Ensure ML systems adhere to ethical guidelines, data privacy regulations, and industry standards

• Design and development of Generative AI and AI use cases (LLMs, RAG, Agentic, multi model AI, fine tuning. Vector databases and prompt engineering)

• Lead organizational change for the adoption of new platforms, machine learning tools and analytics workflows

• Own all communication and collaboration channels pertaining to strategy and assigned projects, including regular stakeholder, senior leadership and cross-team updates

• Establish working relationships with vendors (Technology and Consulting), partners and cross teams and holding them accountable

Basic Qualifications (Required Skills/Experience):

• Bachelor’s degree or higher

• 5+ years of experience with AI/ML technologies, frameworks, models and ensembles

• 5+ years of experience with Pytorch, SciKit Learn, Tensorflow, or similar backend frameworks

• 5+ years of experience with Kubernetes, Docker containers, and Ansible

• 5+ years of experience with data engineering and data pipelines for On-Prem cloud, hybrid data models and data warehouses

• 5+ years of experience with DevOps software including Gitlab, Ansible, Terraform, Jira, Azure DevOps Pipelines, GitHub, AWS CodeBuild, AWS CodePipelines, AWS CodeGuru

• 5+ years of experience with software programming/scripting (such as Python, Unix/Linux type batch scripting, FORTRAN, C / C++)

Preferred Qualifications (Desired Skills/Experience):

• 10 or more years' related work experience or an equivalent combination of education and experience

• 5+ years of experience in the manufacturing or aviation domain

• 5+ years of experience with big data technologies and data engineering practices

• Experience in multi-cloud and hybrid AI architecture

• Experience with generative AI, NLP, computer vision, or reinforcement learning

• Experience with CI/CD pipelines, DevOps practices and containerized deployments

• Experience with fine-tuning and optimization approaches (LoRA / QLoRA, PEFT, parameter-efficient training), and cost/performance tradeo