Vijay Sales

About Vijay Sales

Vijay Sales is one of India’s leading electronics retail brands with 160+ stores nationwide and a fast-growing digital presence. We are on a mission to build the most advanced data-driven retail intelligence ecosystem—using AI, predictive analytics, LLMs, and real-time automation to transform customer experience, supply chain, and omnichannel operations.

Role Overview

We are looking for a highly capable AI Engineer who is passionate about building production-grade AI systems, designing scalable ML architecture, and working with cutting-edge AI/ML tools. This role involves hands-on work with Databricks, SQL, PySpark, modern LLM/GenAI frameworks, and full lifecycle ML system design.

Key Responsibilities

Machine Learning & AI Development

• Build, train, and optimize ML models for forecasting, recommendation, personalization, churn prediction, inventory optimization, anomaly detection, and pricing intelligence.
• Develop GenAI solutions using modern LLM frameworks (e.g., LangChain, LlamaIndex, HuggingFace Transformers).
• Explore and implement RAG (Retrieval Augmented Generation) pipelines for product search, customer assistance, and support automation.
• Fine-tune LLMs on company-specific product and sales datasets (using QLoRA, PEFT, and Transformers).
• Develop scalable feature engineering pipelines leveraging Delta Lake and Databricks Feature Store.

Databricks / Data Engineering

• Build end-to-end ML workflows on Databricks using PySpark, MLflow, Unity Catalog, Delta Live Tables.
• Optimize Databricks clusters for cost, speed, and stability.
• Maintain reusable notebooks and parameterized pipelines for model ingestion, validation, and deployment.
• Use MLflow for tracking experiments, model registry, and lifecycle management.

Data Handling & SQL

• Write advanced SQL for multi-source data exploration, aggregation, and anomaly detection.
• Work on large, complex datasets from ERP, POS, CRM, Website, and Supply Chain systems.
• Automate ingestion of streaming and batch data into Databricks pipelines.

Deployment & MLOps

• Deploy ML models using REST APIs, Databricks Model Serving, Docker, or cloud-native endpoints.
• Build CI/CD pipelines for ML using GitHub Actions, Azure DevOps, or Databricks Workflows.
• Implement model monitoring for drift, accuracy decay, and real-time alerts.
• Maintain GPU/CPU environments for training workflows.
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Must-Have Technical Skills

Core AI/ML

• Strong fundamentals in machine learning: regression, classification, time-series forecasting, clustering.
• Experience in deep learning using PyTorch or TensorFlow/Keras.
• Expertise in LLMs, embeddings, vector databases, and GenAI architecture.
• Hands-on experience with HuggingFace, embedding models, and RAG.

Databricks & Big Data

• Hands-on experience with Databricks (PySpark, SQL, Delta Lake, MLflow, Feature Store).
• Strong understanding of Spark execution, partitioning, and optimization.

Programming

• Strong proficiency in Python.
• Experience writing high-performance SQL with window functions, CTEs, and analytical queries.
• Knowledge of Git, CI/CD, REST APIs, and Docker.

MLOps & Production Engineering

• Experience deploying models to production and monitoring them.
• Familiarity with tools like MLflow, Weights & Biases, or SageMaker equivalents.
• Experience in building automated training pipelines and handling model drift/feedback loops.

Preferred Domain Experience

• Retail/e-commerce analytics
• Demand forecasting
• Inventory optimization
• Customer segmentation & personalization
• Price elasticity and competitive pricing