Principal Machine Learning Engineer Hybrid in Horsham, PA or Remote EST

About Our Team For over 50 years, Lexis Nexis Reed Technology has partnered with the U.S. Patent and Trademark Office (USPTO) to deliver secure, scalable, and high-quality patent data processing solutions. Our work transforms complex, unstructured patent submissions into standardized, searchable outputs that power examiner workflows and public dissemination.

We operate in a highly regulated environment with strict security requirements, large-scale data volumes, and complex business rules. Our focus is on modernizing legacy workflows through automation, AI, and platform transformation to improve efficiency, accuracy, and cost effectiveness.

About the Role Principal AI Engineer – Architecture Track We are seeking a hands-on Principal AI Engineer who can design and build production-grade AI solutions while growing into a broader AI Architect role. This position is ideal for an experienced AI developer or technical lead who has strong engineering depth, understands modern AI architecture, and is ready to expand their influence across platforms, products, and delivery teams.

The Principal AI Engineer will work closely with product managers, architects, data scientists, software engineers, security teams, and government stakeholders to translate mission and business needs into secure, scalable, and reusable AI capabilities. The role will initially focus on solution design and hands-on implementation, with increasing responsibility for architecture standards, technical strategy, governance, and cross-team alignment.
*** Conditions of Employment:

You must be a U.S. citizen to apply for this position.

You must successfully pass a background investigation and achieve Public Trust security clearance.

Must be located near the Horsham, PA location for a hybrid onsite schedule.

Responsibilities AI Solution Design and Development Design, prototype, and implement AI-powered applications, services, agents, and workflows.

Translate business, user, and government mission requirements into practical technical solutions.

Develop solutions using large language models, retrieval-augmented generation, machine learning, natural language processing, computer vision, or other relevant AI technologies.

Build reusable AI components, services, APIs, evaluation frameworks, and reference implementations.

Integrate AI capabilities with enterprise platforms, data sources, workflows, and existing applications.

Balance rapid experimentation with the engineering discipline required for secure, reliable production systems.

Evaluate models, platforms, frameworks, and vendors based on performance, cost, security, scalability, and mission fit.

Architecture and Technical Leadership Contribute to solution architectures covering applications, models, data, integrations, infrastructure, security, and operational monitoring.

Partner with senior architects to establish AI architecture patterns, guardrails, standards, and reference architectures.

Help teams make informed decisions regarding commercial, open-source, and internally developed AI capabilities.

Identify opportunities to create shared AI services and reusable capabilities across products, contracts, and government agencies.

Participate in architecture reviews and clearly document technical decisions, tradeoffs, assumptions, and risks.

Provide technical guidance, code reviews, mentoring, and hands-on support to engineering and data science teams.

Grow into ownership of end-to-end AI solution architecture and broader technical strategy.

Government and Stakeholder Engagement Support technical discovery sessions, demonstrations, proofs of concept, proposals, RFIs, and RFP responses.

Communicate complex AI concepts, limitations, risks, and tradeoffs clearly to both technical and non-technical audiences.

Help move successful prototypes into secure, supportable, and scalable production capabilities.

Stay informed about evolving government AI policies, standards, acquisition practices, and responsible-AI expectations.

Requirement Bachelor’s degree in computer science, engineering, data science, information systems, or a related field, or equivalent practical experience.

Significant professional software engineering experience, including experience delivering production applications or platforms.

Hands-on experience developing AI, machine-learning, or data-intensive solutions.

Proficiency in Python and experience with APIs, cloud services, data pipelines, software development practices, and source control.

Experience with modern…