Artificial Intelligence Engineer

• About Aarvian

Aarvian is a "Process-First" AI transformation partner. We specialize in moving global Retailers from fragmented data to Agentic Workflows. We don't build "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 at Aarvian, you will be responsible for the hands-on development of AI agents and LLM-powered applications. Working within a high-caliber squad of Data Engineers and Scientists, you will turn retail business logic into production-ready code using the Azure Databricks Lakehouse.

Key Responsibilities

• Hands-on Agent Development: Build and iterate on multi-agent workflows (using LangChain or CrewAI) that solve specific retail functions like automated stock replenishment or personalized marketing copy generation.

• Lakehouse Integration: Develop and maintain data pipelines using PySpark and Delta Live Tables to feed high-quality data into AI models.

• RAG Implementation: Build and optimize Retrieval-Augmented Generation (RAG) systems using Azure AI Search and Databricks Vector Search to ensure agents have real-time access to retail catalogs.

• Model Deployment: Deploy LLMs and custom models via Databricks Model Serving (Mosaic AI) and manage the lifecycle using MLflow.

• Client Tech-Stack Alignment: Ensure all code is written to run natively within the client’s Azure environment, adhering to strict data security and Unity Catalog governance.

Technical Requirements (The "Must-Haves")

• Experience: 3–5 years of professional experience in a Data Science or AI Engineering role.

• Coding: Strong proficiency in Python (clean, modular code) and SQL.

• Big Data: 1+ year of hands-on experience with PySpark and Databricks.

• GenAI: Proven experience building at least one LLM-based application (using OpenAI, Llama, or similar) beyond a simple tutorial.

• Cloud: Familiarity with the Azure portal (Blob Storage, Key Vault, Entra ID). What We Look For (The "Aarvian" DNA)

• The "Builder" Mindset: You enjoy the "messy" part of AI—cleaning 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, optimize shelves, and improve customer experience.