Vijay Sales
https://vijaysales.comAbout
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PythonAbout 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.
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
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