Applied ML Engineering, GenAI, AI Agent

Shopify

Confirmed live today High trust

Remote

Quick summary

Work type: Remote
Location: Remote
Posted: 179 days ago
Freshness: Confirmed live today

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Similar $214k

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About Shopify

Shopify is a leading global commerce platform that enables businesses of all sizes to start, grow, and manage their retail operations online and in-person. It provides tools for storefronts, payments, shipping, and marketing to millions of merchants worldwide.

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At a glance

TL;DR · Applied ML Engineering, GenAI, AI Agent

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The Applied ML Engineering - GenAI, AI Agent role involves joining the Engineering & Data team to develop an AI Personal Shopper. As a Machine Learning Engineering lead or individual contributor, you will design and produce scalable AI/ML system architectures while developing and deploying generative AI, natural language processing, and machine learning models. Key responsibilities include implementing data pipelines for fine-tuning LLMs and solving high-impact data problems. You will utilize Python, shell scripting, vector databases, DBT, BigQuery, BigTable, and orchestration tools to manage streaming and batch data pipelines. The work focuses on building a concierge service to provide personalized recommendations and insightful suggestions for customers, specifically addressing the technical challenge of implementing AI systems at scale to transform the online shopping experience.

Skills

Generative AI LLM Machine Learning Python RLHF ETL Vector Databases DBT BigQuery BigTable Shell Scripting GPU Optimization Data Pipelines Orchestration Tools

What you'll do

Develop and deploy Generative AI, natural language processing, and machine learning models.
Design and produce scalable AI/ML system architectures.
Implement data pipelines for fine-tuning Large Language Models (LLMs).
Solve high-impact data problems to deliver business impact through machine learning products.
Train, evaluate, test, and deploy machine learning products at scale.
Drive ETL design decisions using disparate data sources.
Run machine learning in parallel environments using distributed clusters and GPU optimization.

What we're looking for

Mastery in building data products using generative AI, RLHF, and fine-tuning LLMs.
End-to-end experience in training, evaluating, testing, and deploying machine learning products at scale.
Experience in building data pipelines and driving ETL design decisions using disparate data sources.
Proficiency in Python, shell scripting, streaming and batch data pipelines, vector databases, DBT, BigQuery, BigTable, or equivalent.
Proficiency in orchestration tools.
Experience with running machine learning in parallel environments, such as distributed clusters and GPU optimization.
Ability to communicate effectively with both technical and non-technical audiences.
Ability to work in a digital-first, fast-paced environment and handle ambiguity.

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