Sr AI ML Engineer/Lead

As a Senior AI Engineer at our company, you will lead a strategic enterprise engagement focusing on Pricing Elasticity Modeling, Customer Segmentation, and AI-driven Supply Chain Intelligence. Your responsibilities will include: - 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 - 3+ years in AI/ML engineering or applied data science, with 1+ 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. As a Senior AI Engineer at our company, you will lead a strategic enterprise engagement focusing on Pricing Elasticity Modeling, Customer Segmentation, and AI-driven Supply Chain Intelligence. Your responsibilities will include: - 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 - 3+ years in AI/ML engineering or applied data science, with 1+ 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