Senior Software Engineer – AI Infrastructure
New
J
JobgetherAI infrastructure
Based in Brazil; Fully remote, with Brazil included among the eligible hiring locations.Full-TimeSenior
Salary not disclosed
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Job Details
- Experience
- At least five years of experience building, deploying, and operating high-scale production software systems.
- Required Skills
- RustMLOpsDistributed Systems
Requirements
- At least five years of experience building, deploying, and operating high-scale production software systems.
- Strong proficiency in Rust and systems-level programming.
- Deep understanding of distributed systems, including service communication, concurrency, fault tolerance, and reliability engineering.
- Experience optimizing latency, throughput, resource utilization, and operational efficiency.
- Experience operating services that support millions of users or handle high-throughput production workloads.
- Familiarity with machine learning infrastructure, model serving, or MLOps in production environments.
- Experience implementing observability solutions, including monitoring, logging, tracing, alerting, and failure recovery.
- Strong understanding of production operations, incident investigation, system resilience, and performance troubleshooting.
- Experience building infrastructure for AI agents, autonomous workflows, or LLM-powered applications is a plus.
- Background in high-performance networking, asynchronous systems, or low-latency architectures is a plus.
- Experience with container orchestration, cloud-native infrastructure, or modern deployment platforms is a plus.
- Familiarity with model evaluation frameworks, inference benchmarking, and monitoring model performance at scale is a plus.
Responsibilities
- Design, develop, and maintain production infrastructure for AI agent systems.
- Build high-performance Rust backend services for model inference, agent orchestration, and execution workflows.
- Architect scalable distributed systems for high request volumes and millions of users.
- Develop infrastructure for model serving, deployment, evaluation, and production monitoring.
- Optimize inference and orchestration workflows for latency, throughput, resource utilization, and cost efficiency.
- Implement monitoring, logging, tracing, and observability mechanisms.
- Establish guardrails, fault-handling strategies, and recovery mechanisms for agent workflows and distributed services.
- Identify and resolve performance bottlenecks, operational failures, and reliability issues.
- Partner with AI, machine learning, agent systems, and infrastructure teams to deliver production-ready solutions.
- Own critical production systems and contribute to architecture decisions, infrastructure standards, and engineering practices.
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