We are looking for an experienced Principal Engineer – Backend & AI to provide technical leadership across backend engineering, distributed systems, cloud-native platforms, and AI-powered solutions.
The ideal candidate will be a hands-on technology leader with deep expertise in Java/Spring Boot, microservices, scalable architectures, APIs, cloud platforms, and modern AI/GenAI technologies. You will work closely with engineering, product, architecture, and business teams to define technology strategy, solve complex engineering problems, and build highly scalable and intelligent products.
Key Responsibilities
Architecture & Technical Leadership
Define and drive the architecture and technology strategy for highly scalable backend platforms.
Design robust, secure, resilient, and high-performance distributed systems.
Provide technical leadership across multiple engineering teams and complex initiatives.
Establish architecture standards, design principles, coding standards, and engineering best practices.
Drive technology modernization, platform transformation, and cloud adoption.
Review and influence critical architectural and technical decisions across the organization.
Act as a technical mentor and thought leader for Senior Engineers, Tech Leads, and Engineering Managers.
Java Backend Engineering
Design and develop scalable backend services using Java and Spring Boot.
Build and evolve microservices, RESTful APIs, event-driven systems, and distributed applications.
Drive performance optimization, scalability, reliability, and availability of backend platforms.
Work with databases, caching, messaging, and distributed data technologies.
Design solutions for high-throughput and low-latency workloads.
Lead technical initiatives involving refactoring, modernization, and migration of legacy platforms.
AI / Generative AI
Identify opportunities to integrate AI and Generative AI capabilities into products and engineering platforms.
Design and implement AI-powered backend services and intelligent applications.
Work with LLMs, RAG, embeddings, vector databases, prompt engineering, AI agents, and AI orchestration frameworks.
Integrate commercial and/or open-source AI models through APIs and enterprise AI platforms.
Design scalable architectures for AI inference, model integration, evaluation, monitoring, and governance.
Partner with Data Science and ML teams to productionize AI/ML solutions.
Evaluate emerging AI technologies and translate them into practical business and engineering solutions.
Establish best practices for responsible, secure, and cost-effective use of GenAI
