Sr AI ML Engineer

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

· 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.