AI/ML Engineer- MLOps - UPS Digital MARTEC

Ups Job; India; On-site; INR 1,500,000 - 2,500,000; Full time

Job summary

Ups Job in Indien sucht einen erfahrenen AI/ML Engineer mit Schwerpunkt auf MLOps. Sie sind verantwortlich für die Entwicklung und Implementierung von ML-Modellen, die Optimierung des Marketing-Ökosystems und die Automatisierung von Modellen für Echtzeitanwendungen. Diese Position erfordert umfassende Kenntnisse in Datenengineering und ML-Frameworks.

Der ideale Kandidat hat 5–10 Jahre Erfahrung und Kenntnisse in Cloud-Plattformen. In der Rolle arbeiten Sie eng mit Data Scientists und Softwareentwicklern zusammen, um intelligente Systeme zu schaffen.

Qualifications

• Erfahrung in der Bereitstellung von ML-Modellen in Produktionsumgebungen.
• Starke Kenntnisse in Feature Engineering und Dateninfrastruktur.
• Fähigkeit zur Automatisierung von Pipelines für Training und Inferenz.

Responsibilities

• Modellbereitstellung in der Global Customer Platform.
• Automatisierung von Trainings- und Retrainings-Workflows.
• Überwachung der Modellleistung in Produktionsumgebungen.

Skills

Python

ML-Frameworks (z.B. Scikit-learn, TensorFlow)

Data Engineering

MLOps

Cloud-Plattformen (Azure, AWS, GCP)

Education

5–10 Jahre Erfahrung in Datenengineering oder ML-Engineering

Tools

Docker

Kubernetes

Airflow

Job description

## AI/ML Engineer- MLOps - UPS Digital MARTECBewerbenlocations: IN - TDC 1 (IN110)time type: Vollzeitposted on: Vor mehr als 30 Tagen ausgeschriebenjob requisition id: R26006275**Bevor Sie sich auf eine Stelle bewerben, wählen Sie Ihre bevorzugte Sprache aus den Optionen oben rechts auf dieser Seite aus.**Entdecke deine nächste Karrierechance bei einem der größten Logistikdienstleister der Welt. Stelle dir die Vielzahl an Möglichkeiten vor, etwas zu bewegen und werde Teil eines großartigen Teams aus ganz unterschiedlichen Kulturen. Bei uns arbeitest du mit talentierten Kolleginnen und Kollegen, die dir dabei helfen, jeden Tag über dich hinauszuwachsen. Wir wissen, was nötig ist, um UPS in die Zukunft zu führen: Menschen mit einer einzigartigen Kombination aus Können und Leidenschaft. Wenn du die Eigenschaften und die Motivation besitzt, dich selbst oder dein Team zu führen, findest du bei uns den Job, der zu dir passt. Bei UPS erhältst du die Möglichkeit, deine Fähigkeiten unter Beweis zu stellen und deine Karriere auf ein neues Level zu bringen.**Tätigkeitsbeschreibung:**## **About Machine Learning Engineering at UPS Technology:**We’re the obstacle overcomers, the problem get-arounders. From figuring it out to getting it done... our innovative culture demands “yes and how!” We are UPS. We are the United Problem Solvers.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 are 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 Marketing ML Engineer / ML Ops Engineer is responsible for operationalizing machine learning models within the marketing technology ecosystem. This role ensures production-grade deployment, low-latency inference, reliable data refresh cycles, and fully automated model pipelines.The position 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**The ML Engineer will deploy and manage:* 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 pipelinesTheir work directly supports personalization, targeting, retention strategies, and revenue optimization initiatives.## **Key Responsibilities**### 1. Model Deployment & Productionization* 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.### 2. Pipeline 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.### 3. Model Monitoring & Governance* 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.### 4. Feature Engineering & Data Infrastructure* 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**Art der Anstellung:**Unbefristet