Software Engineer II- Java & AI

7-Eleven Global Solution Center – India; Bengaluru; On-site; INR 1,000,000 - 1,800,000; Full time

Job summary

7-Eleven Global Solution Center – India is looking for a Senior AI Backend Engineer to build and deploy ML-driven solutions through scalable Java Spring Boot microservices. The ideal candidate will design real-time data pipelines, collaborate with cross-functional teams to deliver AI features, and optimize model performance. Experience in Java Spring Boot, ML, and platforms like Kafka/Kinesis is essential. This role offers opportunities to work on innovative AI projects in Bengaluru's tech scene.

Qualifications

• Experience in building and deploying ML-driven solutions.
• Proficiency in designing scalable applications using Java Spring Boot.
• Familiarity with CI/CD processes and best coding practices.

Responsibilities

• Design, develop, and maintain Java Spring Boot applications.
• Build end-to-end ML pipelines: data ingestion to deployment.
• Design real-time data pipelines from Kafka/Kinesis streams.
• Translate business problems into measurable ML solutions.
• Collaborate with teams to deliver production-ready AI features.
• Implement model monitoring and performance tracking.
• Optimize model performance for accuracy and cost.
• Maintain CI/CD pipelines to ensure code quality.

Skills

Java Spring Boot development

Machine Learning (ML)

Real-time data processing

Kafka

Kinesis

Job description

Location: Bangalore

Role Description

Job: Senior AI Backend Engineer (ML & Distributed Systems)

Job Description: Senior AI Backend Engineer who can build and deploy ML-driven solutions through scalable Java Spring Boot microservices and real-time data pipelines.

Responsibilities

• Design, develop, and maintain Java Spring Boot applications.
• Build end-to-end ML pipelines: data ingestion → feature engineering → model training → evaluation → deployment.
• Design and implement real-time data pipelines by consuming streams from Kafka/Kinesis, transforming data, and feeding it into ML models and downstream microservices.
• Translate business problems into measurable ML solutions and actionable insights.
• Collaborate with product, data, and engineering teams to ship production-ready AI features.
• Implement model monitoring, performance tracking, and drift detection in production.
• Optimize model performance for accuracy, latency, and cost.
• Maintain CI/CD pipelines and ensure high-quality, testable, and maintainable code.