Job Overview
We are looking for a
Senior Backend Engineer
who thrives on building scalable, high-performance systems and is excited to collaborate in an environment increasingly powered by
AI-driven features and intelligent automation
.
In this role, you’ll work closely with our Product, Data, and Engineering teams to design and implement robust backend services that support core business operations as well as AI-related capabilities like data processing pipelines, model serving infrastructure, and personalization engines.
Our core stack includes
Java, Spring Boot, PostgreSQL, Kafka, Redis
, and
AWS
.
Key Responsibilities
- Design, build, and maintain scalable backend services and APIs that power both core features and AI-enhanced functionalities.
- Build infrastructure and pipelines to support AI/ML operations (data ingestion, pre-processing, feature extraction, model serving).
- Collaborate with AI/ML engineers to productionize machine learning models, ensuring efficient and reliable model integration with backend systems.
- Ensure backend services are optimized for performance, observability, and fault tolerance.
- Lead backend development for intelligent features such as personalized recommendations, search optimization, fraud detection, or smart automation.
- Participate in code reviews, technical discussions, and architecture decisions across teams.
- Own services end-to-end—from design to deployment and monitoring in production.
Technical Requirements
- Strong experience in Java, Spring Boot, Hibernate, and REST API development.
- Proficiency in designing microservices architectures and working with message queues/event streaming (e.g., Kafka).
- Experience with PostgreSQL and caching technologies like Redis.
- Familiarity with AI/ML concepts and how to support AI systems from a backend perspective (e.g., batch vs real-time processing, model versioning, latency optimization).
- Hands-on experience working with cloud platforms (AWS preferred) and tools for monitoring/logging.
- Experience building and scaling data pipelines or integrating ML models into production systems is a plus.
- Good understanding of containerization tools (Docker, Kubernetes preferred).
Nice to Have
- Exposure to AI services and frameworks such as OpenAI, Hugging Face, TensorFlow Serving, or MLflow.
- Experience supporting chatbots, natural language processing (NLP) features, or AI-generated insights.
- Interest or experience in prompt engineering, vector search, or semantic data processing.
- Understanding of data privacy and ethical AI practices.
