Principal Machine Learning Engineer

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.

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

• Competitive salary