Senior Software Engineer

Role Overview

We seek seasoned Senior Cloud-Native Software Engineers to join our product and engineering squad, helping to architect and deliver scalable enterprise platforms. The ideal candidate will bring over a decade of software development experience, robust technical fundamentals, and hands-on skill in crafting cloud-native systems. You'll collaborate within an established team to design, build, deploy, and enhance our platforms continuously.

This hands-on role involves engineering top-quality software and navigating intricate technical challenges. Candidates who align well with our technical standards and actively contribute fresh ideas to refine processes are highly valued. Experience with embedding AI functionality into enterprise software is a strong plus, though solid software engineering expertise remains fundamental.

Key Responsibilities

• Architect, develop, and sustain scalable and resilient cloud-native enterprise-grade applications and platforms.
• Create backend services, APIs, and microservices primarily using Java and/or Python.
• Deploy and manage applications in Kubernetes and cloud environments efficiently.
• Design secure, scalable, and maintainable microservices and API-centric architectures.
• Develop event-driven and asynchronous system components where suitable.
• Implement modern CI/CD pipelines, DevOps practices, automation, rigorous testing, and observability throughout software delivery lifecycle.
• Engage in technical design and architecture discussions balancing quality and practical deadlines.
• Collaborate effectively across product management, architecture, engineering, and stakeholders within the product engineering team.
• Adhere to and enhance engineering standards, development methodologies, and delivery workflows.
• Diagnose and resolve complex system issues to improve reliability, scalability, and maintainability.
• Offer technical mentorship and guidance to fellow engineers as appropriate.
• Continuously research and apply emerging technologies to provide business and engineering benefits.

AI and Emerging Technologies

Experience embedding AI capabilities in enterprise environments is advantageous. Relevant expertise includes:

• Integrating Large Language Models (LLMs) within enterprise software.
• Implementing Retrieval-Augmented Generation (RAG) models.
• Using AI orchestration frameworks and agent-based architectures.
• Connecting with commercial or open-source AI/ML APIs and platforms.
• Delivering robust, secure, scalable, and cost-effective AI-enabled services with governance considerations.

Note: AI skills should complement, not overshadow, strong engineering and architectural prowess.

Required Expertise and Abilities

• Minimum 10 years in professional software engineering building enterprise-level applications.
• Expert coding skills in Java and/or Python.
• Proven background designing and deploying cloud-native and distributed systems.
• Deep knowledge of microservices architectures and API design approaches.
• Hands-on experience operating Kubernetes and containerized environments.
• Familiarity with event-driven systems, messaging, and asynchronous processes.
• Strong proficiency in CI/CD and DevOps frameworks.
• Experience with cloud infrastructures and modern deployment strategies.
• Solid foundations in software engineering practices including OOP, design patterns, clean code, automated testing, version control, security, performance, scalability, observability, and production support.
• Experience working within collaborative, product-focused engineering teams.
• Excellent analytical and problem-solving capabilities.
• Clear communicator skilled in collaborating across technical teams.

Preferred Qualifications

• Hands-on with LLM integration, RAG, AI agent frameworks, or AI orchestration in production.
• Experience with Kafka or similar event streaming technologies.
• Expertise with cloud providers like AWS, Azure, or Google Cloud.
• Practice with infrastructure-as-code and automation tools.
• Exposure to distributed tracing, metrics, logging, and modern observability tools.
• Knowledge of secure software and enterprise security practices.
• Experience in architecting or maintaining large-scale or widely distributed systems.
• Previous technical leadership or mentoring within engineering teams.