Job Title

Over 12 years of extensive experience in AI/ML , with a proven track record of architecting and delivering enterprise-scale machine learning solutions across the Retail and FMCG domains . Demonstrated ability to align AI strategy with business outcomes in areas such as customer experience, dynamic pricing, demand forecasting, assortment planning, and inventory optimization. Deep expertise in Large Language Models (LLMs) and Generative AI , including OpenAI’s GPT family , ChatGPT , and emerging models like DeepSeek . Adept at designing domain-specific use cases such as intelligent product search, contextual recommendation engines, conversational commerce assistants, and automated customer engagement using Retrieval-Augmented Generation (RAG) pipelines. Strong hands-on experience developing and deploying advanced ML models using modern data science stacks including: Python (advanced programming with focus on clean, scalable codebases) TensorFlow and Scikit-learn (for deep learning and classical ML models) NumPy , Pandas (for data wrangling, transformation, and statistical analysis) SQL (for structured data querying, feature engineering, and pipeline optimization) Expert-level understanding of Deep Learning architectures (CNNs, RNNs, Transformers, BERT/GPT), and Natural Language Processing (NLP) techniques such as entity recognition, text summarization, semantic search, and topic modeling – with practical application in retail-focused scenarios like product catalog enrichment, personalized marketing, and voice/text-based customer interactions. Strong data engineering proficiency , with experience designing robust data pipelines, building scalable ETL workflows, and integrating structured and unstructured data from ERP, CRM, POS, and social media platforms. Proven ability to operationalize ML workflows through automated retraining, version control, and model monitoring. Significant experience deploying AI/ML solutions at scale on cloud platforms such as AWS (SageMaker, Bedrock) , Google Cloud Platform (Vertex AI) , and Azure Machine Learning . Skilled in designing cloud-native architectures for low-latency inference, high-volume batch scoring, and streaming analytics. Familiar with containerization (Docker), orchestration (Kubernetes), and CI/CD for ML (MLOps). Ability to lead cross-functional teams , translating technical concepts into business impact, and collaborating with marketing, supply chain, merchandising, and IT stakeholders. Comfortable engaging with executive leadership to influence digital and AI strategies at an enterprise level.