The company is looking to solve the problem of optimizing supply chains and maximizing value recovery through the use of AI/ML capabilities, including generative AI-driven solutions, RAG applications, and predictive models for retail pricing.
Requirements
- Proficiency in Python and AI/ML libraries like TensorFlow, PyTorch, and Scikit-Learn.
- Hands-on experience with LLMs, NLP models, prompt engineering, and tools like OpenAI API, Hugging Face Transformers, LangChain, LlamaIndex, and AI agentic frameworks.
- Strong understanding of data preprocessing, feature engineering, and model selection for time series and pricing data.
- Experience in building and deploying ML models on cloud platforms (AWS SageMaker, GCP Vertex AI, or Azure ML).
- Knowledge of MLOps best practices, including CI/CD pipelines, version control, and model monitoring.
- Experience with AI-driven pricing optimization in retail, logistics, or e-commerce.
- Familiarity with AI agentic frameworks for building autonomous AI agents.
Responsibilities
- Design, build, and deploy predictive models for retail pricing using data from various internal and external sources.
- Develop and fine-tune generative AI models (LLMs) for automation, data augmentation, and content generation.
- Implement RAG (Retrieval-Augmented Generation) applications to enhance AI systems with dynamic information retrieval.
- Build and integrate AI agentic frameworks for autonomous decision-making and task automation.
- Build and maintain scalable machine learning pipelines for data processing, training, and inference.
- Collaborate with cross-functional teams (data engineering, operations, and business) to define AI/ML use cases and deliver solutions.
- Monitor and improve model performance, ensuring robustness, scalability, and reliability.
Other
- 5+ years of experience in AI/ML engineering with a strong focus on generative AI, RAG applications, and predictive modeling.
- Excellent problem-solving skills and ability to communicate complex AI concepts clearly.
- Ability to collaborate with cross-functional teams (data engineering, operations, and business) to define AI/ML use cases and deliver solutions.
- Strong knowledge of AI/ML ethics, ensuring fairness and bias mitigation in models.
- Prior work in AI automation for supply chain, demand forecasting, or pricing strategies.
