Generative AI Engineer

AboutAarvian

Aarvianis a "Process-First" AI transformation partner. We specialize in moving global Retailers from fragmented data toAgentic Workflows. Wedon'tbuild "Black Boxes"; we build bespoke AI solutions directly on our clients' native stacks to drive measurable P&L impact.

The Role

As an AI Engineer atAarvian, you willbe responsible forthe hands-on development of AI agents and LLM-powered applications. Working within a high-calibersquad of Data Engineers and Scientists, you will turn retail business logic into production-ready code using theAzure DatabricksLakehouse.

Key Responsibilities

• Hands-on Agent Development:Build and iterate on multi-agent workflows (usingLangChainorCrewAI) that solve specific retail functions like automated stock replenishment or personalized marketing copy generation.
• Lakehouse Integration:Develop andmaintaindata pipelines usingPySparkandDelta Live Tablesto feed high-quality data into AI models.
• RAG Implementation:Build andoptimizeRetrieval-Augmented Generation (RAG) systems usingAzure AI SearchandDatabricks Vector Searchto ensure agents have real-time access to retailcatalogs.
• Model Deployment:Deploy LLMs and custom models viaDatabricks Model Serving (Mosaic AI)and manage the lifecycle usingMLflow.
• Client Tech-Stack Alignment:Ensure all code is written to run natively within the clientsAzureenvironment, adhering to strict data security andUnityCataloggovernance.

Technical Requirements (The "Must-Haves")

• Experience:24 years of professional experience in a Data Science or AI Engineering role.
• Coding:StrongproficiencyinPython(clean, modular code) andSQL.
• Big Data:1+ year of hands-on experience withPySparkandDatabricks.
• GenAI:Proven experience building at least one LLM-based application (using OpenAI, Llama, or similar) beyond a simple tutorial.
• Cloud:Familiarity with theAzureportal (Blob Storage, Key Vault, Entra ID).

What We Look For (The "Aarvian" DNA)

• The "Builder" Mindset:You enjoy the "messy" part of AIcleaning data, handling edge cases, and making things work in production.
• Rapid Learner:You stay updated on the weekly shifts in the GenAI landscape (new models, new frameworks).
• Retail Curiosity:You are interested in how AI can reduce waste,optimizeshelves, and improve customer experience.