Senior Backend Software Engineer

• 6+ years of professional software engineering experience, with demonstrated technical leadership and expertise in scalable, distributed architectures.
• Strong proficiency in one or more programming languages, such as Python, Go, Java, or C++, with the willingness to learn additional technologies.
• Strong foundation in computer science fundamentals, data structures, and algorithms.
• Proven experience building and operating scalable systems handling thousands of requests per second (RPS), with high reliability targets such as 99.99% availability.
• Experience designing complex distributed systems that process substantial data volumes or support millions of users.
• Hands-on experience developing low-latency, high-throughput microservices and deploying production-grade systems.
• Strong knowledge of distributed storage and database technologies, including SQL and NoSQL databases such as PostgreSQL and Cassandra.
• Experience building and maintaining large-scale or real-time data processing pipelines.
• Strong understanding of production monitoring, observability, performance optimisation, and system reliability.
• Ability to break down complex business requirements into scalable technical solutions and lead implementation effectively.

• Design, build, and maintain highly available, secure authentication systems and API services.
• Develop and operate low-latency microservices capable of handling high-throughput workloads.
• Maintain and evolve mission-critical internal databases, backend services, and distributed architectures.
• Ensure production systems meet stringent standards for reliability, scalability, performance, and security.

• Architect, optimise, and operate high-volume, auto-scaling infrastructure for real-time data ingestion and processing.
• Build and maintain systems that process substantial data volumes, including several terabytes per day, or support millions of users.
• Develop and improve machine learning inference infrastructure and other latency-sensitive backend services.
• Implement monitoring, observability, and reliability practices for production-grade distributed systems.
• Contribute to cloud infrastructure and deployment automation using modern DevOps tools and practices.

• Design and develop scalable search systems and agentic workflows capable of supporting high levels of concurrency.
• Improve large language model (LLM) accuracy, cost efficiency, and scalability through effective prompt and context engineering.
• Work on capabilities such as data ingestion, text summarisation, semantic search, traditional search, and agent orchestration.
• Optimise system performance and resource utilisation for AI-powered applications.

• Build and maintain large-scale and real-time data processing pipelines.
• Work with relational and NoSQL databases to support high-volume, distributed workloads.
• Design and optimise data processing architectures using technologies such as PostgreSQL, Kafka, Flink, Snowflake, Hadoop, and ZooKeeper.
• Ensure data systems deliver reliable ingestion, processing, storage, and retrieval at scale.

• Build scalable observability systems that provide detailed insights into customer usage, system performance, and service reliability.
• Establish effective monitoring and troubleshooting practices across distributed services.
• Create and maintain technical documentation for internal systems, public APIs, and backend services.

• Lead the decomposition and resolution of complex technical and business problems.
• Drive architectural decisions and contribute to engineering best practices.
• Mentor engineers and collaborate with cross-functional teams to deliver reliable, production-ready solutions.
• Take ownership of system design, implementation, deployment, and ongoing optimisation.

• Python
• Go
• Java

• Node.js
• React
• Redis

The engineering environment includes:

• Programming Languages: Go, Python, Node.js
• Frontend Technologies: React
• Databases and Storage: Cassandra, Redis, SQL databases
• Cloud and Infrastructure: AWS, Terraform
• Containerisation and Orchestration: Docker, Kubernetes
• Streaming and Networking: Kafka, Envoy

• Experience building large-scale search or agentic AI systems.
• Familiarity with semantic search, text summarisation, prompt engineering, context engineering, and agent orchestration.
• Experience with machine learning inference systems and real-time data ingestion infrastructure.
• Knowledge of distributed data processing technologies such as Flink, Snowflake, Hadoop, and ZooKeeper.