Anthrobyte - Senior AI/ML Engineer - Pricing Elasticity Modeling

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.

Key Responsibilities :

- 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 :

- 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 :

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 :

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 :

- 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 :

- 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.