Principal Machine Learning Engineer

Benefits:

• Competitive salary

Location

Remote to start, then becomes hybrid in Raleigh, NC (relocation required; candidates able to relocate at the start of the contract are preferred, though relocation may occur upon conversion to full-time).

Experience Level

Principal Level (10+ years of experience in machine learning/AI engineering).

Role Overview

We are seeking a Principal Machine Learning Engineer to architect, develop, and advise on end-to-end AI solutions leveraging large language models (LLMs), Retrieval-Augmented Generation (RAG), and agentic systems. This is a senior individual contributor role for someone who wants to remain highly technical rather than move into people management, while providing technical strategy and architectural guidance to a growing data science team. The ideal candidate has played a lead role in designing and establishing a new agentic RAG-based system, with demonstrated ownership of meaningful architectural and technical decisions - not simply a contributor within a larger team.

Key Responsibilities

AI Architecture & Solution Design

• Architect, develop, and advise on end-to-end AI solutions leveraging LLMs, RAG, agentic systems, and cloud-scale infrastructure.


• Construct, study, and train algorithms that learn from complex, high-dimensionality data to uncover patterns for predictive models and applications.


• Apply techniques such as random forests, deep learning, generative modeling, and neural network memory to improve NLP and machine perception algorithms.

Proof of Concept & Technology Evaluation

• Develop proofs of concept and initial implementations for new AI capabilities.


• Evaluate and test-drive new frameworks and tools to inform technical direction.

Technical Strategy & Leadership

• Work closely with architects to guide teams on AI system design and best practices.


• Provide technical strategy and stay current with industry standards in AI and machine learning.


• Understand the full environment and systems end to end, ensuring production readiness.


• Align AI system design with business and technical goals.

Required Qualifications

• Deep expertise in LLMs, RAG, and agentic systems.


• Experience with Model Context Protocol (MCP) and vector databases.


• Strong experience with cloud platforms (AWS, Azure, or GCP).


• Strong Python development skills and AI architecture experience.


• Experience with MLOps and software engineering best practices.


• Strong data engineering skills.


• Demonstrated experience designing, architecting, and implementing a new agentic RAG-based system, with evidence of meaningful architectural and technical decision-making (not solely as a contributor within a larger team).


• Engineering experience beyond core data science, including microservices architecture and cloud computing.

Preferred Qualifications

• Experience with GoLang.


• Experience working with unstructured data and document-heavy domains.


• Experience supporting or mentoring less senior data scientists/engineers as a technical lead.

Core Skills & Attributes

• Strong technical depth with a preference for remaining hands-on rather than pursuing people management.


• Proven ability to make and own architectural and technical decisions on complex AI systems.


• Strong collaboration skills, particularly working closely with architects and cross-functional teams.


• Comfortable evaluating and adopting emerging tools and frameworks.


• Excellent communication skills for translating technical strategy across teams.


• Strong end-to-end systems thinking across AI architecture and production environments.

This is a remote position.

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