Job VC
Middle ML Engineer (e-commerce)
Technologies
Description
Zipify builds high-impact Shopify apps that help merchants maximize revenue and conversion. Our flagship product, OneClickUpsell (OCU), has generated over $1.2B in upsell revenue for merchants. We are evolving beyond traditional SaaS into a hybrid model that combines product, AI, and high-value services to deliver measurable growth for ecommerce brands.
We’re looking for an AI/ML Engineer to join our product team to operate across Zipify’s product ecosystem, including OCU, Zipify Pages, and emerging AI-driven products. This is not a classic ML role — we’re looking for someone with a strong AI engineering mindset: building intelligent systems, working with LLMs, and turning models into real product features.
This is a hands-on role at the intersection of AI/ML, product, and data. The ideal candidate is comfortable building AI-powered functionality, working with structured data, and contributing to data-related engineering tasks when needed.
What We Value in a New Master
3+ years Python hands-on experience with pandas and NumPy.
1+ years of ML experience: classification, clustering, regression — and hands-on experience building such models.
Strong SQL skills and comfort working with databases in day-to-day tasks.
Experience working with LLM APIs and prompt engineering.
Understanding of how to design and build practical agentic systems.
Comfort working with real-world product data and data pipelines.
Git, Docker — comfortable day-to-day usage.
English level: Upper-Intermediate or higher.
Nice to Have
AWS stack: SageMaker, S3, Lambda, and related services.
Data engineering experience: Airflow, dbt.
Experience with recommender systems, experimentation, or A/B testing.
Experience with Streamlit.
Background in e-commerce, SaaS, marketplace, or other product-driven environments.
Portfolio with relevant projects.
How You’ll Contribute
Design and build AI-powered features, including
LLM-based
and agentic systems.
Integrate and work with LLM APIs (OpenAI, Claude, etc.).
Identify patterns and dependencies in structured and behavioral data to generate actionable insights.
Build recommendation and decisioning systems that improve user interaction with the product.
Design workflows that combine LLMs, structured data, and business logic.
Collaborate with data and product teams to bring experiments into production.
Contribute across the full cycle — from prototype to deployed solution, evaluation, monitoring, and iteration.
We’re looking for an AI/ML Engineer to join our product team to operate across Zipify’s product ecosystem, including OCU, Zipify Pages, and emerging AI-driven products. This is not a classic ML role — we’re looking for someone with a strong AI engineering mindset: building intelligent systems, working with LLMs, and turning models into real product features.
This is a hands-on role at the intersection of AI/ML, product, and data. The ideal candidate is comfortable building AI-powered functionality, working with structured data, and contributing to data-related engineering tasks when needed.
What We Value in a New Master
3+ years Python hands-on experience with pandas and NumPy.
1+ years of ML experience: classification, clustering, regression — and hands-on experience building such models.
Strong SQL skills and comfort working with databases in day-to-day tasks.
Experience working with LLM APIs and prompt engineering.
Understanding of how to design and build practical agentic systems.
Comfort working with real-world product data and data pipelines.
Git, Docker — comfortable day-to-day usage.
English level: Upper-Intermediate or higher.
Nice to Have
AWS stack: SageMaker, S3, Lambda, and related services.
Data engineering experience: Airflow, dbt.
Experience with recommender systems, experimentation, or A/B testing.
Experience with Streamlit.
Background in e-commerce, SaaS, marketplace, or other product-driven environments.
Portfolio with relevant projects.
How You’ll Contribute
Design and build AI-powered features, including
LLM-based
and agentic systems.
Integrate and work with LLM APIs (OpenAI, Claude, etc.).
Identify patterns and dependencies in structured and behavioral data to generate actionable insights.
Build recommendation and decisioning systems that improve user interaction with the product.
Design workflows that combine LLMs, structured data, and business logic.
Collaborate with data and product teams to bring experiments into production.
Contribute across the full cycle — from prototype to deployed solution, evaluation, monitoring, and iteration.