Description

What's the Job?

• Architect and implement Python agent frameworks, including tool adapters, orchestration loops, structured outputs, session handling, and production-ready CLI or service interfaces.
• Build LLM-powered workflows using frameworks such as LiteLLM, DSPy, or equivalent, with bounded prompts, allowlisted tools, budget caps, structured JSON outputs, and evidence-backed reporting.
• Integrate agents with the AMD GPU and ML software ecosystem, including ROCm tooling, PyTorch, distributed training and inference stacks, profilers, cluster metrics, log pipelines, and debug utilities.
• Own quality and evaluation infrastructure, including fixture-driven tests, JSON Schema validation, regression suites, accuracy metrics, and CI integration prior to rollout.
• Solve ML operations challenges on training and inference clusters, including multi-node failure analysis, performance regression investigation, configuration and launch issues, and operational automation.

What's Needed?

• Bachelor's degree or higher in Computer Science, Computer Engineering, Electrical Engineering, or a related field; equivalent practical experience accepted.
• 10+ years of professional software engineering experience, including 5+ years developing production Python systems at scale.
• Deep experience with distributed ML training or inference on GPU clusters, including multi-node jobs, collective communication failures, and log/metric correlation across ranks and nodes.
• Strong hands-on experience with PyTorch and production familiarity with large-scale stacks such as Megatron-LM, DeepSpeed, TorchTitan, vLLM, or equivalent.
• Proven ML debugging and performance engineering experience across throughput regressions, memory errors, numerical instability, misconfiguration, and profiler/trace analysis

Key Skills

Education

Any Graduate