Data Scientist - Machine Learning Models

Job description : We are looking for Data Scientist with 3 to 5 Yrs in Retail Domain. Job Type : FullTime. Job Mode : Work From Office. Notice Period : Immediate to 20 Days. Location : Hyderabad. Data Scientist JD : - 3 to 5 years' experience in building Predictive AI and Generative AI solutions. - Design, develop, and deploy generative AI models for text, image, audio, or video generation - Fine-tune large language models (LLMs) like GPT, LLaMA, Anthropic Claude, or open-source models for specific use cases - Develop and optimize prompt engineering techniques to improve model outputs - Build AI-driven chatbots, virtual assistants, and Agents - Train models using datasets and reinforcement learning techniques - Work with frameworks such as TensorFlow, PyTorch, and Hugging Face Transformers - Deploy AI models using cloud services (AWS, GCP, Azure) or on-premises infrastructure - Develop APIs and integrate AI models into applications Ensure model efficiency, scalability, and ethical AI compliance - Experience with Python, TensorFlow, PyTorch, and Hugging Face libraries - Strong knowledge of transformer models, LLM fine-tuning, and diffusion models - Experience with LLM APIs (OpenAI, Anthropic, Mistral, etc.) - Understanding of prompt engineering, retrieval-augmented generation (RAG), and embeddings - Proficiency in REST APIs, cloud services (AWS, GCP, Azure), and containerization (Docker, Kubernetes) Key skills : ML (Machine Learning). NLP (Natural Language Processing). Computer Vision. Retail Domain expertise. Concepts of LLM good to have. Python , R , SQL. Cloud Good to have : Retail projects could be like Customer segmentation , CRM , POS , Demand forecasting , Inventory Optimisation , supply chain analytics, omnichannel retail strategies, and loyalty programs). Key Responsibilities: - Design, develop, and deploy generative AI models for various modalities, including text, image, audio, and video generation. - Fine-tune large language models (LLMs) such as GPT, LLaMA, Anthropic Claude, or open-source alternatives for specific retail use cases (e.g., product description generation, personalized marketing content, virtual customer assistants). - Develop and optimize prompt engineering techniques to enhance the quality and relevance of model outputs. - Build AI-driven chatbots, virtual assistants, and intelligent agents for customer service, sales, and support. - Implement retrieval-augmented generation (RAG) to improve the grounding and accuracy of LLM outputs. - Develop and deploy predictive models for various retail applications, including demand forecasting, inventory optimization, customer segmentation, CRM, and POS analytics. - Apply machine learning techniques to analyze customer behavior, sales trends, and market data to drive actionable insights. - Develop models for customer churn prediction, lifetime value estimation, and personalized recommendations. - Optimize supply chain analytics through predictive modeling and optimization algorithms. - Train and evaluate AI models using large datasets and reinforcement learning techniques. - Work with deep learning frameworks such as TensorFlow and PyTorch, and utilize libraries like Hugging Face Transformers. - Deploy AI models using cloud services (AWS, GCP, Azure) or on-premises infrastructure. - Develop REST APIs to integrate AI models into existing applications and systems. - Ensure model efficiency, scalability, and adherence to ethical AI compliance standards. - Containerization with Docker and Kubernetes. - Apply domain knowledge to solve real-world retail problems, including customer segmentation, CRM, POS, demand forecasting, inventory optimization, supply chain analytics, omnichannel retail strategies, and loyalty programs. - Collaborate with business stakeholders to understand requirements and translate them into effective AI solutions. - Develop and implement AI solutions to optimize retail operations, enhance customer experience, and drive revenue growth. - Perform data cleaning, preprocessing, and feature engineering to prepare data for model training. - Utilize SQL to extract, transform, and load data from various sources. - Perform exploratory data analysis (EDA) to identify patterns and insights. (ref:hirist.tech)

Location: mumbai, IN

Posted Date: 5/1/2025