Principal Applied AI Engineer

Bullhorn•Remote NOAM - Missouri, MO

1d•$175,000 - $200,000

About The Position

The Principal Applied AI Engineer will be a strategic and influential hands-on-keyboard individual contributor, responsible for architecting practical AI solutions that enable product leaders and teams to meet their goals. They will guide the teams to develop and maintain a core platform of reusable tools—including a central LLM client and evaluation harness—that dramatically speeds up development, and ensures the quality of, AI backed features. Critically, this role requires the strategic wisdom to determine the right tool for the job, making data-supported decisions for when a frontier LLM (commercial or open source) is the best approach versus when a bespoke machine learning model offers a faster, cheaper, and more effective solution. The Principal Engineer will act as the primary technical consultant for product and architecture leaders, ensuring every AI-powered feature is built on a foundation of excellence, while providing clear oversight of the cost, quality, and business value of all AI systems.

Requirements

Pragmatic Innovator: A leader who is passionate about cutting-edge AI but is fundamentally driven by solving real-world problems and delivering business value.
Deep Technical Expertise: Extensive hands-on experience in both modern LLM-based systems (prompt engineering, RAG, fine-tuning, agentic workflows) and traditional machine learning (e.g., classification, regression, NLP).
Platform Builder: Proven experience designing, building, and maintaining reusable software components, libraries, and APIs that are used by other engineering teams.
Consultative Leader: Exceptional communication skills with the ability to explain complex technical trade-offs to both technical and non-technical stakeholders.
Data-Driven: Obsessed with measurement. Has a track record of implementing systems to track performance, quality, and cost, and using that data to make informed decisions.

Nice To Haves

Experience acting as an internal consultant or solutions architect is highly desirable.

Responsibilities

Serve as the primary point of contact and principal consultant for product and architecture leaders during the design and implementation of all AI systems.
Champion a pragmatic, results-oriented approach to AI, ensuring the team focuses on practical (Applied) solutions that enable product teams to build high quality AI backed features.
Establish the strategic framework for deciding when to use large language models versus bespoke ML models or traditional NLP, based on performance, cost, and speed.
Lead the technical development of dedicated/bespoke ML models for specialized tasks where they are the superior option.
Oversee the evolution of internal services into a robust, central hub for all LLM interactions.
Drive the creation and maintenance of a standardized LLM client that provides a unified API for interacting with various models (e.g., OpenAI, Anthropic, open-source).
Drive the creation, maintenance and expansion of a catalog of reusable code libraries, tools, and well-vetted prompts that provide product teams with an “80% solution” to accelerate their development cycles.
Drive the standardization of an internal skills library for development. Diagnose issues preventing adoption and advise on fixes and improvements for the continuous improvement of the standard toolset
Own and enhance the model evaluation harness to allow for rapid prototyping, A/B testing, and quality measurement of AI solutions.
Own the AI gateway solution to support users, refine configuration, and monitor efficiency and cost.
Provide regular, clear insights to leadership on the value, ROI, and total cost of ownership of our AI initiatives.
Establish and govern best practices for the entire ML lifecycle, including prompt engineering, data privacy, model safety, and MLOps.