Sr AI ML Engineer/Lead

About the Role We are seeking a Senior AI Engineer to lead a strategic enterprise engagement spanning Pricing Elasticity Modeling, Customer Segmentation, and AI-driven Supply Chain Intelligence. You will deliver production-grade ML/LLM solutions. Requirements Key Responsibilities Pricing Elasticity Modeling Build econometric and ML models for price elasticity across products, channels, and segments. Develop dynamic and competitive pricing using regression, Bayesian, causal inference, and RL techniques. Create scenario simulation tools to forecast revenue and margin impact of pricing strategies. Customer Segmentation Develop behavioral, RFM, lifecycle, and propensity-based segmentation using clustering and embeddings. Leverage LLMs to enrich customer representations from unstructured data (reviews, tickets, communications). Operationalize segments into CRM, CDP, and personalization platforms for marketing and sales activation. AI-Driven Supply Chain Intelligence Architect demand forecasting using classical (ARIMA, Prophet) and deep learning (LSTM, TFT, N-BEATS) methods. Build inventory optimization, replenishment, and supplier risk models using ML and operations research. Design LLM-powered agents and copilots for analytics, anomaly detection, and decision support. Technical Leadership & Delivery Own end-to-end solution design data ingestion, feature engineering, deployment, monitoring, governance. Mentor data scientists/ML engineers; lead code, model, and design reviews. Establish MLOps/LLMOps best practices: CI/CD, versioning, drift detection, and responsible AI guardrails. Required Qualifications 4+ years in AI/ML engineering or applied data science, with 2+ year of production LLM experience. Bachelor's or Master's in CS, Data Science, Statistics, OR, Economics, or related quantitative field. Strong Python skills with scikit-learn, XGBoost/LightGBM, and PyTorch or TensorFlow. Hands-on with LLM tooling: LangChain/LlamaIndex, Hugging Face, OpenAI/Anthropic/Bedrock/Vertex APIs. Experience building RAG systems, agentic workflows, and prompt engineering for enterprise use cases. Solid grounding in statistics, time-series forecasting, and causal inference. Production deployment experience on AWS (SageMaker), Azure ML, or GCP Vertex AI. Strong SQL and modern data stack exposure: Snowflake, Databricks, BigQuery, or Redshift. MLOps experience with MLflow, Airflow, Docker, Kubernetes, and CI/CD pipelines. Excellent stakeholder communication; able to present to executive audiences. Preferred Qualifications Domain experience in retail, CPG, e-commerce, manufacturing, or logistics. Familiarity with vector DBs (Pinecone, Weaviate, FAISS, pgvector), uplift modeling, and bandits. Cloud certifications: AWS ML Specialty, Azure AI Engineer, or GCP ML Engineer. Prior consulting or client-facing delivery leadership; OSS contributions or publications a plus. About the Role We are seeking a Senior AI Engineer to lead a strategic enterprise engagement spanning Pricing Elasticity Modeling, Customer Segmentation, and AI-driven Supply Chain Intelligence. You will deliver production-grade ML/LLM solutions. Requirements Key Responsibilities Pricing Elasticity Modeling Build econometric and ML models for price elasticity across products, channels, and segments. Develop dynamic and competitive pricing using regression, Bayesian, causal inference, and RL techniques. Create scenario simulation tools to forecast revenue and margin impact of pricing strategies. Customer Segmentation Develop behavioral, RFM, lifecycle, and propensity-based segmentation using clustering and embeddings. Leverage LLMs to enrich customer representations from unstructured data (reviews, tickets, communications). Operationalize segments into CRM, CDP, and personalization platforms for marketing and sales activation. AI-Driven Supply Chain Intelligence Architect demand forecasting using classical (ARIMA, Prophet) and deep learning (LSTM, TFT, N-BEATS) methods. Build inventory optimization, replenishment, and supplier risk models using ML and operations research. Design LLM-powered agents and copilots for analytics, anomaly detection, and decision support. Technical Leadership & Delivery Own end-to-end solution design data ingestion, feature engineering, deployment, monitoring, governance. Mentor data scientists/ML engineers; lead code, model, and design reviews. Establish MLOps/LLMOps best practices: CI/CD, versioning, drift detection, and responsible AI guardrails. Required Qualifications 4+ years in AI/ML engineering or applied data science, with 2+ year of production LLM experience. Bachelor's or Master's in CS, Data Science, Statistics, OR, Economics, or related quantitative field. Strong Python skills with scikit-learn, XGBoost/LightGBM, and PyTorch or TensorFlow. Hands-on with LLM tooling: LangChain/LlamaIndex, Hugging Face, Op