AI/ML Engineer

Primary Responsibilities/Essential Functions

• 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.

Qualifications

• 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.