AI/ML Engineer

Job Description 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 ecosystemusing 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 Skills:- Python, Artificial Intelligence (AI), Generative AI, databricks and Data Visualization