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Team: IT
This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Software Engineer, Infrastructure (Bengaluru) based in India.
This is a high-impact infrastructure engineering role focused on building the foundational systems that power large-scale data and AI workloads. You will work on deeply technical challenges spanning distributed systems, cloud infrastructure, and production-grade Kubernetes environments. The role sits at the intersection of scalability, reliability, and developer productivity, enabling fast and safe research-to-production cycles. Your work will directly support exabyte-scale data systems and enterprise AI applications operating in demanding production environments. This is not an operations or feature-support role, but a core engineering position with significant ownership and architectural responsibility. You will collaborate with senior engineers to design systems that are resilient, cost-efficient, and highly automated. The environment is fast-moving, technically rigorous, and centered on long-term infrastructure impact.
Accountabilities:
- Design, build, and operate scalable cloud infrastructure supporting petabyte to exabyte-scale data systems.
- Manage and evolve production Kubernetes clusters ensuring high availability, reliability, and efficient scaling.
- Develop and optimize CI/CD pipelines to improve build speed, deployment safety, and release consistency.
- Enhance developer productivity by automating workflows and reducing operational friction across engineering teams.
- Build and maintain observability systems across logging, metrics, tracing, and alerting for production environments.
- Contribute to end-to-end testing frameworks and release confidence mechanisms to ensure system stability.
- Collaborate with engineering teams to solve complex architectural, scaling, and reliability challenges.
- Own customer-facing infrastructure integration, including deployment support and troubleshooting in external environments.
- Drive infrastructure best practices around reliability, automation, performance, and operational excellence.
- Debug and resolve complex cross-layer issues across networking, storage, runtime, and distributed systems layers.
- 5+ years of experience in infrastructure engineering, platform engineering, or distributed systems development.
- Strong programming skills in Go, Java, Python, or similar languages.
- Proven experience managing production-grade Kubernetes environments in cloud platforms such as AWS, GCP, or Azure.
- Strong expertise in CI/CD systems and modern deployment pipelines.
- Hands-on experience with infrastructure-as-code tools such as Terraform.
- Ability to diagnose and debug complex distributed systems issues across multiple layers.
- Strong understanding of cloud infrastructure, containers, and scalable system design principles.
- Demonstrated ability to build reliable, high-performance, and cost-efficient systems.
- Experience working in fast-paced, high-ownership startup or product engineering environments.
- Strong communication skills and ability to collaborate across distributed engineering teams.
- Exposure to data platforms or lakehouse technologies is a plus.
- Familiarity with tools such as Spark, Trino, Iceberg, Delta, Airflow, or observability stacks (Prometheus, Grafana, ELK, Datadog) is a bonus.
- Competitive compensation with meaningful equity and performance-based bonuses.
- Flexible time off to support work-life balance.
- Comprehensive health coverage for you and your family.
- Opportunity to work on large-scale, cutting-edge AI and data infrastructure systems.
- High ownership role with direct impact on core platform architecture.
- Support for deep technical exploration, research, and professional growth.
- Collaborative engineering culture focused on excellence and innovation.
- Exposure to complex distributed systems challenges at scale.
- Opportunity to shape foundational infrastructure powering enterprise AI.
- Fast-paced environment with strong emphasis on engineering autonomy and impact.
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