Sr. Machine Learning Engineer (Data Science)

While technology is the heart of our business, a global and diverse culture is the heart of our success. We love our people and we take pride in catering them to a culture built on transparency, diversity, integrity, learning and growth.


If working in an environment that encourages you to innovate and excel, not just in professional but personal life, interests you- you would enjoy your career with Quantiphi!

About Quantiphi:

Quantiphi is an award-winning, AI-First global digital engineering company that helps the world’s leading Fortune 1000 organizations transform bold ideas into measurable business impact. We go beyond building innovative AI technologies—we solve the problems that matter most to our clients.

Since our founding in 2013, Quantiphi has built a proven track record of turning complex challenges into meaningful outcomes across industries.

Headquartered in Boston, with more than 4,000 professionals worldwide, we partner with global enterprises to deliver large-scale digital, cloud, and AI-driven transformation. #SolvingWhatMatters

We are an Elite and Premier partner to Google Cloud, AWS, NVIDIA, Snowflake, and other leading technology platforms, and our work has been recognized across the industry, including:

• 21 Google Cloud Partner of the Year awards in the past 10 years

• 3 AWS AI/ML Partner of the Year awards

• 3 NVIDIA Partner of the Year awards

• 3 Snowflake Partner of the Year awards

• Rated Leaders by Gartner, Forrester, IDC, ISG, Everest Group and other leading analyst firms

Quantiphi delivers First-in-class AI solutions across Life Sciences, Healthcare, Banking, Financial Services, CPG, Manufacturing, Energy, High-Tech, Telecommunications, etc., powered by cutting-edge Generative AI and Agentic AI accelerators.

We are also proud to be certified as a Great Place to Work—reflecting our commitment to our people and our culture.

For more details, visit: Website or LinkedIn Page

Role: Sr. Machine Learning Engineer (Data Science)

Experience Level: 5+ Years

Employment type: Full Time

Location: California

Role Summary

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 architec