Senior Backend Engineer
Build and lead high-performance Golang backend services for an AI compute and PaaS platform. The role requires 8+ years of backend or infrastructure experience, distributed-systems expertise, and strong cloud-native, Kubernetes, and container orchestration skills.
About the job
Responsibilities
Core Platform & Infrastructure Backend
- Architect and develop high-performance Golang services for an AI PaaS and infrastructure platform.
- Build internal APIs for model deployment, job scheduling, and compute lifecycle management.
- Develop components interfacing with GPU/compute infrastructure and AI runtimes.
Distributed Systems & Scalability
- Design and scale microservices and event-driven systems for high-throughput AI workloads.
- Optimize for low latency, high concurrency, and fault tolerance.
- Implement service-to-service communication using gRPC, REST, message queues, and asynchronous pipelines.
- Drive reliability, observability, and resilience across services.
AI Platform Integration
- Integrate systems with training pipelines, inference infrastructure, experimentation workflows, and dataset/artifact management.
- Enable orchestration across cloud and on-premises environments.
- Build abstractions that simplify AI infrastructure consumption.
Cloud-Native & Platform Engineering
- Design cloud-native, Kubernetes-native services.
- Work with DevOps/SRE on CI/CD, deployment automation, and scalability.
- Contribute to architecture decisions for multi-region, multi-cloud infrastructure.
- Improve monitoring, logging, and diagnostics.
Technical Leadership
- Lead architecture reviews and establish engineering standards.
- Mentor engineers and guide complex problem-solving.
- Drive the long-term roadmap for backend infrastructure and AI platform capabilities.
- Partner with Product, Runtime, and Infrastructure leadership to translate requirements into scalable systems.
Requirements
- 8+ years of backend or infrastructure engineering experience.
- Expert-level, hands-on proficiency in Golang.
- Strong experience building production-grade distributed systems.
- Proven experience with infrastructure platforms, PaaS, or deep-tech systems.
- Deep understanding of cloud-native architectures and containerized environments.
- Strong experience with Kubernetes, Docker, and cluster orchestration.
- Experience with distributed databases such as PostgreSQL, Cassandra, or DynamoDB.
- Strong understanding of caching, queues, and streaming systems such as Redis and Kafka.
- Python experience as a secondary language.
Nice-to-Haves
- Experience with compute scheduling, resource management, or platform runtimes.
- Experience with AI/ML platforms, model infrastructure, or data platforms.
- Familiarity with ML pipelines, inference systems, or GPU-backed workloads.
- Exposure to PyTorch, TensorFlow infrastructure, or model-serving systems.
- Experience with multi-cloud or on-premises infrastructure.
- Experience with Prometheus, Grafana, OpenTelemetry, or similar observability tools.
- Experience with gRPC, REST, microservices, event-driven systems, message queues, asynchronous pipelines, and streaming systems.
Compensation & Benefits
- No compensation or benefits information was provided.
Skills
Go, Python, Kubernetes, Docker, AWS, GCP, Microsoft Azure, gRPC, Rest, Postgres, Cassandra, DynamoDB, Redis, Kafka, Prometheus
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