AI Enablement Engineer

Galaxy

About The Position

We're seeking an AI Enablement Engineer to join our team in building, deploying, and scaling AI solutions across the enterprise. You'll work at the intersection of machine learning engineering, data engineering, and platform operations—building RAG systems, evaluating cutting-edge LLM models, and serving as a trusted advisor to business units and corporate functions navigating their AI adoption journey.

Requirements

6+ years of experience in any of the following: Site reliability engineering (SRE), Software engineering, Data engineering (DBA/DBRE), or Machine learning engineering
Strong proficiency in Python and experience building data pipelines
Hands-on experience with LLMs and understanding of prompting techniques, fine-tuning, and RAG architectures
Experience with vector databases (e.g., Pinecone, Weaviate, pgvector, ChromaDB)
Familiarity with cloud platforms, particularly AWS services
Excellent communication skills with ability to explain technical concepts to non-technical stakeholders
Demonstrated ability to work independently and drive projects from concept to production

Nice To Haves

Experience with Amazon Bedrock, SageMaker, or similar managed ML services
Familiarity with Ollama or other local LLM deployment frameworks
Background in evaluating and benchmarking ML models
Experience with AI-assisted coding tools (Claude Code, Cursor, GitHub Copilot, etc.)
Understanding of MLOps practices and tools
Previous consulting or internal enablement experience
Knowledge of responsible AI practices and governance frameworks

Responsibilities

Design, build, and optimize Retrieval-Augmented Generation (RAG) systems for various enterprise use cases
Develop and maintain data ingestion pipelines (Python and other scripting languages) to populate and manage vector databases
Deploy and maintain AI infrastructure including Amazon Bedrock and Ollama environments
Implement evaluation frameworks and tooling (such as LLMComparator) to systematically compare LLM performance across different models and use cases
Conduct rigorous evaluations of LLM models for diverse applications including RAG, coding agents, and domain-specific tasks
Establish benchmarking standards and best practices for model selection
Stay current with the rapidly evolving LLM landscape and provide recommendations on emerging capabilities
Partner with teams, business units, and corporate functions to assess their AI needs and design appropriate solutions
Provide guidance on build vs. buy decisions—advising when third-party GenAI tools are appropriate versus when internal capabilities should be leveraged
Enable teams in adopting AI-assisted development tools including Claude Code, Cursor, and GitHub Copilot
Develop documentation, training materials, and best practices for enterprise AI adoption
Ensure reliability, performance, and cost-effectiveness of AI infrastructure and services
Monitor and optimize vector database performance and model inference costs
Collaborate with security and compliance teams to ensure responsible AI deployment