What specific AI agent frameworks does Quantiphi use for the Sr. Machine Learning Engineer role?

For this Sr. Machine Learning Engineer position, Quantiphi utilizes agent frameworks such as LangChain, Vertex AI Agents, and CrewAI. Candidates are expected to have experience building or integrating with these or similar agentic orchestration tools.

What are the primary responsibilities of a Sr. Machine Learning Engineer at Quantiphi in this role?

The primary responsibilities include designing and deploying forecasting models, building AI agents for workflow automation, developing production ML pipelines on GCP, and integrating ML outputs into agentic workflows for autonomous decision-making.

What level of experience is required for the Sr. Machine Learning Engineer role at Quantiphi?

Quantiphi requires a minimum of 6 years of experience in machine learning engineering and data science for this Sr. Machine Learning Engineer position. A strong portfolio of deployed forecasting or predictive models is also essential.

Does Quantiphi offer opportunities for professional development for a Sr. Machine Learning Engineer?

Yes, Quantiphi emphasizes continuous upskilling and provides hands-on experience with cutting-edge AI, ML, data, and cloud technologies, allowing Sr. Machine Learning Engineers to stay ahead of the curve.

What statistical and time-series analysis knowledge is needed for the Sr. Machine Learning Engineer position?

A strong foundation in statistics, probability, and time-series analysis is crucial. This includes knowledge of models like ARIMA, Prophet, exponential smoothing, and state-space models, as well as hypothesis testing and causal inference.

Is there an opportunity to work with LLMs in this Sr. Machine Learning Engineer role?

Yes, preferred qualifications include familiarity with LLM fine-tuning, prompt engineering, and retrieval-augmented generation (RAG) patterns, suggesting potential involvement with large language models for enterprise AI agents.

What certifications would be beneficial for the Sr. Machine Learning Engineer role at Quantiphi?

A Google Cloud Professional Machine Learning Engineer or Professional Data Engineer certification is considered a preferred qualification for this role, indicating a strong advantage for candidates holding these credentials.

What is the engagement type and location for the Sr. Machine Learning Engineer role at Quantiphi?

This is a contract engagement for a Sr. Machine Learning Engineer role, classified as Professional Services / Consulting Delivery. The position is remote, with potential for onsite travel as required.

Sr. Machine Learning Engineer (Data Science)

Quantiphi · United States

Hybrid
Full-time
$150,000 / year
United States

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Job highlights

Develop advanced forecasting and automation agents.
Utilize Google Cloud Platform services.
Engineer production ML pipelines.
Collaborate with data engineering teams.
Optimize model performance and document findings.

About the role

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 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

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.

Engagement Details

Client Industry: Global Technology Distribution & Solutions
Delivery Partner: Quantiphi (an AI-First Digital Engineering company)
Cloud Platform: Google Cloud Platform (GCP)
Engagement Type: Professional Services / Consulting Delivery
Location: Remote with potential onsite travel as required
Duration: Contract engagement aligned with project milestones

What’s in it for YOU at Quantiphi?

Join one of the world’s fastest-growing AI-first digital engineering companies and make a real impact at scale.
Lead and collaborate with a high-energy team of talented, driven individuals solving complex, meaningful challenges.
Work with Fortune 500 companies and disruptive innovators in a research-driven environment with 60+ patents.
Stay ahead of the curve by gaining hands-on experience with cutting-edge AI, ML, data, and cloud technologies while continuously upskilling.

If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!

Key skills/competency

Machine Learning Engineering
Data Science
Forecasting Models
AI Agents
Google Cloud Platform (GCP)
Vertex AI
Python
SQL
MLOps
Time-Series Analysis

Skills & topics

Machine Learning Engineer
Data Science
Forecasting
AI Agents
Google Cloud Platform
GCP
Vertex AI
Python
SQL
MLOps
Time Series Analysis
Predictive Modeling
Cloud Computing
Digital Engineering

How to get hired

Tailor your resume: Highlight your 6+ years in ML engineering, forecasting models, Python, and GCP experience.
Showcase your portfolio: Include deployed forecasting or predictive models and any relevant certifications.
Emphasize GCP expertise: Detail your hands-on experience with Vertex AI, BigQuery ML, and other GCP services.
Prepare for technical questions: Be ready to discuss statistics, time-series analysis, and AI agent frameworks.
Highlight consulting skills: If applicable, showcase client-facing and Agile delivery experience.

Technical preparation

Master Python, Pandas, NumPy, scikit-learn.,Deepen GCP ML stack knowledge.,Practice SQL for large datasets.,Study time-series and forecasting models.

Behavioral questions

Describe a complex ML project you led.,How do you handle model performance issues?,Explain a time you collaborated with data engineers.,How do you present technical findings to stakeholders?

Frequently asked questions

What specific AI agent frameworks does Quantiphi use for the Sr. Machine Learning Engineer role?: For this Sr. Machine Learning Engineer position, Quantiphi utilizes agent frameworks such as LangChain, Vertex AI Agents, and CrewAI. Candidates are expected to have experience building or integrating with these or similar agentic orchestration tools.
What are the primary responsibilities of a Sr. Machine Learning Engineer at Quantiphi in this role?: The primary responsibilities include designing and deploying forecasting models, building AI agents for workflow automation, developing production ML pipelines on GCP, and integrating ML outputs into agentic workflows for autonomous decision-making.
What level of experience is required for the Sr. Machine Learning Engineer role at Quantiphi?: Quantiphi requires a minimum of 6 years of experience in machine learning engineering and data science for this Sr. Machine Learning Engineer position. A strong portfolio of deployed forecasting or predictive models is also essential.
Does Quantiphi offer opportunities for professional development for a Sr. Machine Learning Engineer?: Yes, Quantiphi emphasizes continuous upskilling and provides hands-on experience with cutting-edge AI, ML, data, and cloud technologies, allowing Sr. Machine Learning Engineers to stay ahead of the curve.
What statistical and time-series analysis knowledge is needed for the Sr. Machine Learning Engineer position?: A strong foundation in statistics, probability, and time-series analysis is crucial. This includes knowledge of models like ARIMA, Prophet, exponential smoothing, and state-space models, as well as hypothesis testing and causal inference.
Is there an opportunity to work with LLMs in this Sr. Machine Learning Engineer role?: Yes, preferred qualifications include familiarity with LLM fine-tuning, prompt engineering, and retrieval-augmented generation (RAG) patterns, suggesting potential involvement with large language models for enterprise AI agents.
What certifications would be beneficial for the Sr. Machine Learning Engineer role at Quantiphi?: A Google Cloud Professional Machine Learning Engineer or Professional Data Engineer certification is considered a preferred qualification for this role, indicating a strong advantage for candidates holding these credentials.
What is the engagement type and location for the Sr. Machine Learning Engineer role at Quantiphi?: This is a contract engagement for a Sr. Machine Learning Engineer role, classified as Professional Services / Consulting Delivery. The position is remote, with potential for onsite travel as required.

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