Principal Machine Learning Engineer

SMART TECH SKILLS LLC

1d•Hybrid

About The Position

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.

Requirements

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.
10+ years of experience in machine learning/AI engineering.

Nice To Haves

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.

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.