Sr Software Engineer Cloud infrastructure

About the Role

The Applied Intelligence Data Engineering team is seeking a Senior Software Engineer - Cloud Infrastructure. This hybrid role blends deep software engineering with hands‑on ownership of cloud infrastructure. It is purpose‑built for engineers who write production Java services and design multi‑cloud platform architecture.

• Own end‑to‑end performance of data streaming applications running on cloud infrastructure - from Kafka topic configuration through consumer processing and downstream delivery.
• Profile and tune streaming pipelines to maximize throughput and minimize latency, leveraging cloud‑native compute, storage, and networking resources.
• Identify and address bottlenecks at the intersection of application code and cloud resource constraints, including CPU throttling, network saturation, I/O limits, and memory constraints.
• Design and implement cloud resource utilization strategies. This includes spot/preemptible instances, managed streaming services, and dynamic node pool scaling to balance performance with cost efficiency.
• Benchmark streaming pipelines end‑to‑end and translate findings into actionable infrastructure and code improvements.
• Collaborate with Data and AI/ML engineering teams to ensure streaming pipelines are optimally provisioned for real‑time feature engineering, inference, and analytics workloads.

• Develop high‑throughput, low‑latency streaming applications using Java and Kafka.
• Design event‑driven microservices that process, enrich, and route real‑time data at scale.
• Implement reactive, non‑blocking architectures to support high concurrency and resilience.
• Develop reusable streaming frameworks, libraries, and platform capabilities to improve engineering velocity and standardization.

• Architect, implement, and optimize multi‑cloud infrastructure across GCP and OCI to support large‑scale data and streaming workloads.
• Design and implement advanced networking architectures, including VPC peering, VPNs, load balancers, and cross‑region failover strategies.
• Build and maintain Terraform‑based infrastructure‑as‑code frameworks to standardize deployments and enable developer self‑service.
• Define autoscaling, deployment, failover, and resource optimization strategies for high‑volume production systems.
• Contribute to platform‑wide architecture decisions related to scalability, resiliency, high availability, and disaster recovery.

• Deploy and manage containerized microservices within Kubernetes environments (GKE, OKE) across cloud platforms.
• Implement container orchestration best practices, service‑mesh configurations, and rolling‑deployment strategies.
• Partner with platform engineering teams to improve developer tooling, deployment automation, and runtime reliability.

• Ensure production‑grade reliability, observability, and operational maturity across streaming platforms and infrastructure.
• Implement comprehensive observability using Prometheus, Grafana, centralized logging, distributed tracing, and health monitoring.
• Optimize systems for throughput, latency, resiliency, resource efficiency, and cloud cost governance.
• Build automated testing strategies for streaming and infrastructure workflows, including unit, integration, contract, chaos, and performance testing.
• Lead incident response, root‑cause analysis, and post‑mortems to improve uptime and reduce operational risk.

• Partner with Data Engineering teams to integrate streaming architectures with batch processing, data lakes, and analytical platforms.
• Collaborate with Software Engineering, Product Management, and API teams to enable real‑time services and data‑driven applications.
• Work closely with AI/ML engineering teams to support real‑time feature engineering, inference pipelines, and operational AI workloads.
• Clearly communicate technical trade‑offs to engineering stakeholders. Also, discuss scalability considerations and operational risks.

• Lead architectural discussions,…