We are a leading provider of reverse logistics and returns management solutions leveraging technology to optimize supply chains and maximize value recovery. We are expanding our AI/ML capabilities to include generative AI-driven solutions RAG applications and predictive models for retail pricing using collected data from multiple sources.

We are seeking an AI/ML Engineer with expertise in generative AI RAG applications AI agentic frameworks and predictive modeling. This role will focus on developing pricing models for retail products enhancing operational efficiency through AI automation and applying cutting-edge techniques in LLMs NLP and agentic AI frameworks.

Primary Responsibilities/Essential Functions

This job description in no way states or implies that these are the only duties to be performed by the teammate occupying this position. The selected candidate may perform other related duties assigned to meet the ongoing needs of the business.

Key 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.

• Utilize tools like OpenAI API Hugging Face LangChain LlamaIndex and cloud platforms (AWS Azure GCP) for AI development and deployment.

Required:

5 years of experience in AI/ML engineering with a strong focus on generative AI RAG applications and predictive modeling.

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.

Excellent problem-solving skills and ability to communicate complex AI concepts clearly.

Preferred:

Experience with AI-driven pricing optimization in retail logistics or e-commerce.

Experience developing and deploying RAG systems for dynamic content retrieval.

Familiarity with AI agentic frameworks for building autonomous AI agents.

Prior work in AI automation for supply chain demand forecasting or pricing strategies.

Strong knowledge of AI/ML ethics ensuring fairness and bias mitigation in models.