Software Engineer

The Challenge

We're looking for a Senior Software Engineer that will report to the Development Manager / R&D Head. In this role, you will part of the R&D Team that works on mission-critical applications.

Your Mission

• Engage and partner with various Engineering, Operations, and Product teams to design, deliver, and maintain a highly available and performant application platform.
• Build and implement application observability and platform monitoring tools to continuously improve the customer experience.
• Eliminate toil by automating processes, tuning alerts, and improving code where it is most needed.
• Frequently evaluate new ideas and trends to identify potentially useful tools and techniques.
• Collaborate with different functional groups to identify gaps, prioritize, and resolve issues.
• Defining, implementing, and maintaining SLIs and SLOs aligned with customer experience.
• Design and instrument SLIs such as latency, error rates, and availability across critical services.
• Manage and enforce error budgets to balance system reliability with product feature velocity.
• Improving alert quality by reducing noise and focusing on actionable, high-signal alerts.
• Embed with product teams to review architectures and catch reliability risks early.
• Share your knowledge and experience with the Engineering organization.
• Share your findings with technical leadership and senior management.
• Build scripts in python/bash/java or ruby for operational automation and incident response.

Your Experience Includes

• Bachelor's degree in computer science, Engineering, or related technical or business field.
• 4+ yrs. of application development experience with Java or other equivalent language.
• Experience with Spring environment.
• Experience in cloud-based infrastructure (Azure, AWS, GCP, etc.).
• Experience with the factors that affect software application performance at different levels. These factors include database performance, network performance, CPU utilization, JVM tuning, memory analysis, thread management, and query performance.
• A knowledge of the importance of centralizing logging, metrics dashboards, and alerting. Able to articulate about some of the tools used for these tasks.
• A good awareness of databases (ideally SQL/NoSQL).
• Hands-on experience with observability tools (Datadog, Prometheus, Grafana, etc.).
• Knowledge with CI/CD pipelines and infrastructure-as-code (Terraform, Helm, jenkins, gitlab).
• Build and operate AI-assisted incident response systems (root cause analysis, log summarization, anomaly triage).
• Develop or integrate LLM-based tools to reduce MTTR and improve alert quality.
• Apply machine learning techniques for anomaly detection, capacity prediction, or failure pattern analysis.
• Experience deploying AI systems in production (not just experimentation).
• Knowledge with vector databases, embeddings, or RAG architectures for operational intelligence.
• Well-developed insight of prompt engineering and evaluation of LLM outputs in the reliability workflow.
• Kubernetes and container orchestration (EKS/AKS/GKE).
• Experience with distributed systems at scale.
• Familiarity with service meshes and microservices architectures.

Nice to Have

• Experience with chaos engineering tools (Gremlin, Chaos Monkey).
• Background in product-facing services with high traffic scale.
• Understand how to use incident management platforms. This includes using tools like PagerDuty for alerts. It also includes working with DataDog for monitoring.