Sr. Machine Learning Engineer (Data Science)

Job summary

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.

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
• 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: Qua*** (an AI-First Digital Engineering company)

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 Qua*** leadership.
• Contribute to MLOps best practices including model versioning, drift detection, monitoring dashboards, and automated alerting using Vertex AI Model Monitoring.
• Required

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