Sr. Machine Learning Engineer (Data Science)

Quantiphi is an award-winning, AI-First global digital engineering company that helps Fortune 1000 organizations transform bold ideas into measurable business impact. They are seeking a Sr. Machine Learning Engineer with strong data science expertise to develop intelligent forecasting models and quotation automation agents on Google Cloud Platform.

Responsibilities:

• Design, develop, and deploy forecasting models (time-series, demand forecasting, regression-based) for product demand, pricing trends, and quotation accuracy using GCP-native services (Vertex AI, BigQuery ML)
• Conduct exploratory data analysis (EDA), feature engineering, and hypothesis testing on large-scale distribution and supply chain datasets to surface actionable insights for AI agent decision logic
• Build AI agents for forecasting and quotation workflows using agentic frameworks (LangChain, Vertex AI Agents, CrewAI) with data-driven decision-making capabilities embedded in agent reasoning
• Develop and maintain production ML pipelines on Vertex AI Pipelines and Cloud Composer for model training, evaluation, deployment, and retraining automation
• Implement statistical experimentation frameworks (A/B testing, causal inference) to validate model improvements and measure business impact of forecasting agents
• Collaborate with data engineering teams to design feature stores and data pipelines in BigQuery and Cloud Storage that feed forecasting and quotation models
• Optimize model performance through hyperparameter tuning, cross-validation, ensemble methods, and model interpretability techniques (SHAP, LIME) for stakeholder transparency
• Integrate ML model outputs into agentic workflows, enabling agents to autonomously generate, validate, and refine quotations based on real-time market and inventory data
• Document model architectures, experiment results, and agent decision logic; present findings and recommendations to client stakeholders and Quantiphi leadership
• Contribute to MLOps best practices including model versioning, drift detection, monitoring dashboards, and automated alerting using Vertex AI Model Monitoring

Requirements:

• 6+ years of experience in machine learning engineering and data science, with a strong portfolio of deployed forecasting or predictive models
• Proficiency in Python (Pandas, NumPy, scikit-learn, statsmodels) and at least one deep learning framework (TensorFlow, PyTorch, or JAX)
• Hands-on experience with GCP ML stack: Vertex AI (Training, Prediction, Pipelines), BigQuery, Cloud Functions, Cloud Storage, and Pub/Sub
• Strong foundation in statistics, probability, and time-series analysis (ARIMA, Prophet, exponential smoothing, state-space models)
• Experience building or integrating with AI agent frameworks (LangChain, LlamaIndex, Vertex AI Agents, or similar agentic orchestration tools)
• Proficiency in SQL for complex analytical queries on large-scale data warehouses
• Experience with experiment tracking and model management tools (MLflow, Vertex AI Experiments, Weights & Biases)
• Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, or a related quantitative field
• Google Cloud Professional Machine Learning Engineer or Professional Data Engineer certification
• Experience in supply chain, distribution, or logistics domain with demand forecasting use cases
• Familiarity with LLM fine-tuning, prompt engineering, and retrieval-augmented generation (RAG) patterns for enterprise AI agents
• Prior consulting or professional services experience with client-facing delivery in an Agile environment