Principal Machine Learning Engineer

SMART TECH SKILLS LLC is a technology and professional services firm specializing in innovative technologies. The company is seeking a Principal Machine Learning Engineer to architect, develop, and advise on end-to-end AI solutions using LLMs, RAG, agentic systems, and cloud-scale infrastructure. The role provides hands-on technical strategy, architectural guidance, proof-of-concept development, and production-readiness oversight.

Responsibilities:

• 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
• Develop proofs of concept and initial implementations for new AI capabilities
• Evaluate and test-drive new frameworks and tools to inform technical direction
• 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

Requirements:

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