Description :

Platform and Distributed Systems Architecture :

- Architect, design, and scale large-scale distributed systems that power enterprise-grade AI products.

- Lead the development of high-performance, highly available, and fault-tolerant platforms capable of handling large-scale enterprise workloads.

- Design and evolve microservice-based architectures, APIs, and data pipelines with a strong emphasis on scalability, reliability, and maintainability.

- Drive system-level decisions across compute, storage, networking, and data layers.

AI-First Product Engineering :

- Build and evolve product-first AI systems that are deeply integrated into enterprise workflows.

- Collaborate closely with AI/ML teams to operationalize models into production-grade systems.

- Ensure AI capabilities are delivered as core product features, not standalone services.

- Enable seamless integration between AI inference, data platforms, and customer-facing applications.

Infrastructure, Cloud, and Scalability :

- Lead architecture and implementation across cloud platforms (AWS, GCP, Azure).

- Design scalable infrastructure with a strong focus on cost efficiency, performance, and resilience.

- Own deployment strategies, CI/CD pipelines, observability, and system monitoring.

- Ensure enterprise-grade standards for security, compliance, and data privacy.

Reliability, Observability, and Performance :

- Establish robust monitoring, logging, and alerting systems to ensure platform reliability.

- Drive performance optimization across services, APIs, and data pipelines.

- Proactively identify bottlenecks and improve system stability under high concurrency.

- Build processes and tooling to support operational excellence at scale.

Leadership and Collaboration :

- Lead, mentor, and grow a team of strong backend and platform engineers.

- Set a high engineering bar through design reviews, code reviews, and technical mentorship.

- Partner closely with product, AI/ML, and business teams to translate requirements into scalable technical solutions.

- Influence technical roadmap decisions and guide long-term platform strategy.

Requirements :

- 10-12 years of hands-on software engineering experience, with a strong background in building and scaling enterprise products.

- Systems Expertise : Proven experience designing and operating large-scale distributed systems in production.

- Infrastructure Skills : Deep understanding of cloud-native architectures across AWS, GCP, or Azure.

- Hands-on Engineering : Strong proficiency in Python, Go, Java, or equivalent backend languages.

- Architecture and Design : Solid experience with microservices, APIs, data stores, and asynchronous systems.

- Mindset : Product-first thinking, strong execution capability, and a passion for building AI-driven platforms that scale.

Nice to Have :

- Experience working on AI-powered enterprise or SaaS products.

- Exposure to high-throughput, low-latency systems.

- Experience working in fast-paced startup or 01 environments.

- Strong understanding of system design trade-offs and performance tuning.