Sr. Machine Learning Engineer (Data Science)

Quantiphi; Irvine (CA); On-site; USD 140,000 - 210,000; Full time

Job summary

Quantiphi seeks a Sr. Machine Learning Engineer with deep data science expertise to support an AI agents engagement for a global technology distributor.

The role emphasizes developing intelligent forecasting models and quotation automation on Google Cloud Platform, blending strong statistical modeling with production ML engineering for operational efficiency. Ideal candidates will demonstrate experience in time-series forecasting, GCP ML stack, and building agentic AI workflows, with a track

Qualifications

• 6+ years of experience in ML engineering and data science with deployed forecasting or predictive models.
• Proficiency in Python (Pandas, NumPy, scikit-learn) and at least one deep learning framework (TensorFlow, PyTorch, or JAX).
• Hands-on experience with GCP ML stack: Vertex AI, BigQuery, Cloud Functions, Cloud Storage, Pub/Sub.
• Strong foundation in statistics, probability, and time-series analysis (ARIMA, Prophet, etc.).
• Experience with AI agent frameworks (LangChain, Vertex AI Agents, etc.).
• Proficiency in SQL for complex analytical queries.
• Experience with MLflow, Vertex AI Experiments, Weights & Biases.

Responsibilities

• Design, develop, and deploy forecasting models for product demand and quotation accuracy using GCP-native services.
• Conduct EDA, feature engineering, and hypothesis testing on distribution & supply chain data.
• Build AI agents for forecasting and quotation workflows with agentic frameworks.
• Develop and maintain production ML pipelines on Vertex AI Pipelines and Cloud Composer.
• Implement experimentation frameworks (A/B testing, causal inference) to validate model improvements.
• Collaborate with data engineering to design feature stores and data pipelines in BigQuery/Cloud Storage.
• Optimize model performance with hyperparameter tuning, cross-validation, ensembling, and interpretability (SHAP, LIME).
• Integrate model outputs into agentic workflows to generate and refine quotations.
• Document architectures, experiments, and agent logic; present findings to stakeholders.
• Contribute to MLOps best practices including model versioning, drift detection, and monitoring.

Skills

Python programming

Statistical modeling

Time-series analysis

SQL

Experiment tracking

MLOps

Education

Bachelor's or Master's in CS/Stats/Math

Tools

Vertex AI

BigQuery

Vertex AI Pipelines

Cloud Composer

LangChain

LlamaIndex

Weights & Biases

MLflow

CrewAI

Job description

Quantiphi is seeking a Sr. Machine Learning Engineer with strong data science expertise to support an AI agents engagement with a leading global technology distribution and solutions company. This role will focus on developing intelligent forecasting models and quotation automation agents on Google Cloud Platform (GCP). The ideal candidate combines deep statistical modeling skills with production ML engineering to deliver data-driven agentic AI solutions that drive operational efficiency across the client's distribution ecosystem.

Key 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.

Required Qualifications

• 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.

Preferred Qualifications

• 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.