This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Software Engineer (Machine Learning & Recommendation) based in India.
Join a high-impact engineering team focused on building intelligent recommendation systems that enhance the user experience of a large-scale global marketplace. In this role, you'll design, develop, and optimize production-grade machine learning solutions that improve content discovery and customer engagement. Working with modern cloud infrastructure, Kubernetes, and advanced ML technologies, you'll collaborate with cross-functional teams to solve complex technical challenges at scale. This is an excellent opportunity for an experienced machine learning engineer who enjoys combining software engineering, data science, and MLOps to deliver innovative, reliable, and scalable AI-powered products. The position follows a hybrid work model based in Bangalore, offering flexibility while fostering strong collaboration and technical excellence.
Accountabilities:
- Design, develop, deploy, and maintain scalable machine learning systems focused on recommendation and personalization for large-scale production environments.
- Build and optimize recommendation algorithms that improve user discovery, engagement, and overall marketplace experience.
- Collaborate closely with product managers, software engineers, and cross-functional stakeholders to translate business needs into effective machine learning solutions.
- Analyze large datasets, conduct experiments, and perform A/B testing to evaluate model performance and continuously improve recommendation quality.
- Deploy and operate machine learning models using cloud-native infrastructure, Kubernetes, and modern MLOps practices while ensuring reliability and scalability.
- Monitor production systems, troubleshoot issues, and continuously enhance system performance through data-driven improvements.
- Stay current with advances in artificial intelligence, machine learning, and recommendation technologies, introducing innovative approaches where appropriate.
Requirements
- 5–9 years of professional experience developing and deploying large-scale machine learning systems in production environments.
- Proven experience delivering end-to-end machine learning solutions, including experimentation, model deployment, backend engineering, and MLOps.
- Strong proficiency with machine learning frameworks such as TensorFlow or PyTorch, along with libraries including scikit-learn, NumPy, and pandas.
- Solid understanding of machine learning algorithms, software engineering principles, and production system operations.
- Experience with monitoring, logging, and maintaining reliable production environments.
- Strong communication and collaboration skills with the ability to work effectively across multidisciplinary teams.
- Experience building recommendation systems using large-scale datasets is highly desirable.
- Familiarity with Docker, Kubernetes, cloud platforms (AWS, GCP, or Azure), microservices, enterprise search technologies, or production deployment of deep learning models and LLMs is a plus.
- Research publications in leading peer-reviewed conferences or journals are considered an advantage.
Benefits
- Hybrid work model based in Bangalore (2 days in office, 3 days remote).
- Flexible working hours with a full flextime policy outside of team collaboration meetings.
- Opportunity to work on large-scale machine learning systems with cutting-edge AI technologies.
- Exposure to modern cloud infrastructure, Kubernetes, MLOps, and production-grade recommendation platforms.
- Collaborative, international engineering environment focused on innovation, ownership, and continuous learning.
- Opportunities for technical growth, cross-functional collaboration, and long-term career development.
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