we are seeking a backend engineer with a strong foundation in building scalable, high-performance systems and a deep understanding of cloud infrastructure, distributed systems, and data pipelines. This role focuses on designing and optimizing backend services that support our machine learning (ML) operations and real-time personalization capabilities.
We foster a professional environment where experienced engineers collaborate to drive technical excellence, continuously improving our backend architecture and infrastructure. As a Backend Engineer, you will play a key role in building and maintaining the backend services that power our ML infrastructure, ensuring efficiency, scalability, and reliability.
Role & Responsibilities:
Design, develop, and optimize backend services that support ML pipelines, APIs, and real-time decision-making systems.
Architect and implement scalable and reliable data processing workflows, integrating ML models into production environments.
Build and maintain infrastructure for efficient model deployment, monitoring, and versioning.
Ensure high availability, performance, and security of backend services.
Lead initiatives to improve system architecture, reduce technical debt, and enhance development processes.
Collaborate with data scientists, ML engineers, and DevOps teams to streamline ML model integration.
Stay up to date with the latest advancements in backend technologies, cloud computing, and distributed systems.
R-263410
Requirements: 4+ years of experience in backend engineering, designing and developing distributed systems.
Strong proficiency in Python (preferred), Java, or Go for backend development.
Deep experience with cloud platforms (AWS, GCP, or Azure), including compute, storage, and networking services.
Experience with containerization and orchestration (Docker, Kubernetes).
Proficiency in designing and managing scalable databases (SQL & NoSQL: MySQL, PostgreSQL, Redis, Cassandra, etc.).
Hands-on experience with CI/CD pipelines, infrastructure as code (Terraform, CloudFormation), and automated deployments.
Familiarity with high-performance APIs and microservices architecture.
Experience working with ML operations (MLOps) and data pipelines is a plus but not required.
This position is open to all candidates.