AI/ML Engineer- MLOps - UPS Digital MARTEC

UPS; Chennai; On-site; INR 1,500,000 - 2,500,000; Full time

Job summary

A leading logistics company in Chennai is seeking a Senior Machine Learning Engineer to design, develop, and deploy ML models that drive business outcomes. The ideal candidate will have 5–10+ years in data engineering and experience in deploying ML models into production environments. This role will focus on operationalizing machine learning models within marketing technology, ensuring low-latency inference and automated pipelines. Candidates should be proficient in Python and various ML frameworks, along with knowledge of cloud platforms, containerization, and orchestration tools.

Qualifications

• 5–10+ years in data engineering, ML engineering, or MLOps roles.
• Strong experience deploying ML models into production environments.
• Familiarity with containerization and deployment.

Responsibilities

• Deploy ML models into the Global Customer Platform.
• Ensure low-latency inference for real-time decisioning.
• Monitor model performance in production environments.

Skills

Python

ML frameworks (Scikit-learn, XGBoost, TensorFlow, PyTorch)

Containerization (Docker, Kubernetes)

Orchestration tools (Airflow, Kubeflow)

Cloud platforms (Azure, AWS, GCP)

Job description

Fiche De Poste

Our Machine Learning Engineering teams use their expertise in data science, software engineering, and AI to build next-generation intelligent systems. These systems power our Smart Logistics Network, optimize UPS Airlines, and enhance Global Transportation Operations. We build scalable, production‑grade ML solutions that move up to 38 million packages a day (4.7 billion annually), delivering measurable impact across the enterprise.

About this Role

We’re seeking passionate Senior Machine Learning Engineers to design, develop, and deploy ML models and pipelines that drive business outcomes. You’ll work closely with data scientists, software engineers, and product teams to build intelligent systems that are robust, scalable, and aligned with UPS’s strategic goals.

You will contribute across the full ML lifecycle—from data exploration and feature engineering to model training, evaluation, deployment, and monitoring. You’ll also help shape our MLOps practices and mentor junior engineers.

Job Summary

The ML Engineer will operationalize machine learning models within the marketing technology ecosystem, ensuring production‑grade deployment, low‑latency inference, reliable data refresh cycles, and fully automated model pipelines. This role bridges Data Science and Engineering by transforming experimental models into scalable, monitored, and business‑ready solutions within the Global Customer Platform.

What They Will Build & Operationalize

• Production‑ready marketing ML models, including:
  • Propensity to Buy (PTB)
  • Churn Prediction
  • Customer Lifetime Value (CLV)
• Automated training and inference pipelines
• Real‑time or batch scoring workflows
• Feature store infrastructure for reusable, governed features
• Model monitoring and drift detection systems
• CI/CD‑enabled ML deployment pipelines

Key Responsibilities

• Deploy ML models into the Global Customer Platform.
• Ensure low‑latency inference for real‑time decisioning where required.
• Enable scalable batch scoring pipelines.
• Eliminate manual scoring processes through automation.
• Build automated training and retraining workflows.
• Develop CI/CD pipelines for ML lifecycle management.
• Ensure consistent data refresh cycles aligned with SLA requirements.
• Reduce operational handoffs between Data Science and Engineering teams.
• Monitor model performance in production environments.
• Detect and mitigate model drift (data drift & concept drift).
• Track prediction accuracy, stability, and bias metrics.
• Maintain versioning and reproducibility standards.
• Design and maintain feature stores.
• Ensure feature consistency between training and inference environments.
• Optimize data pipelines for reliability and scalability.Collaborate with data engineering teams on data schema and quality controls.

Required Skills & Experience

• 5–10+ years in data engineering, ML engineering, or MLOps roles
• Strong experience deploying ML models into production environments
• Proficiency in Python and ML frameworks (e.g., Scikit‑learn, XGBoost, TensorFlow, PyTorch)
• Experience with orchestration tools (Airflow, Kubeflow, or similar)
• Familiarity with containerization and deployment (Docker, Kubernetes)
• Experience with cloud platforms (Azure, AWS, or GCP)
• Strong understanding of feature stores and model lifecycle management
• Knowledge of monitoring tools for drift detection and model performance

Preferred Qualifications

• Experience working in marketing analytics or customer data platforms
• Familiarity with CDP integrations and real‑time personalization systems
• Understanding of customer segmentation and campaign activation workflows
• Experience implementing ML governance and compliance standards

Type De Contrat

en CDI

Chez UPS, égalité des chances, traitement équitable et environnement de travail inclusif sont des valeurs clefs auxquelles nous sommes attachés.