Quantiphi
Sr. Machine Learning Engineer (Data Science)
USA - Remote · Senior · Full-time
Sponsorship not specifiedDetected 5 days ago
PythonSQLBigQuerySnowflakeAWSGCPCloud PlatformsMachine LearningDeep LearningTensorFlowPyTorchscikit-learnPandasNumPyData AnalysisData EngineeringData ScienceLLMsRAGAgentic AIMLOpsStatisticsA/B TestingForecasting
About the role
- 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!
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).
- 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
- Document model architectures, experiment results, and agent decision logic; present findings and recommendations to client stakeholders and Quantiphi leadership.
Requirements
- Hands-on experience with GCP ML stack: Vertex AI (Training, Prediction, Pipelines), BigQuery, Cloud Functions, Cloud Storage, and Pub/Sub.
- 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.
- Stay ahead of the curve by gaining hands-on experience with cutting-edge AI, ML, data, and cloud technologies while continuously upskilling.
Nice to have
- 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.
- Engagement Details
- Client Industry: Global Technology Distribution & Solutions
- Cloud Platform: Google Cloud Platform (GCP)
- Engagement Type: Professional Services / Consulting Delivery
- Duration: Contract engagement aligned with project milestones
Skills
- 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
- Google Cloud Platform (GCP)
Company info
- Quantiphi (an AI-First Digital Engineering company)
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