AI Systems & Platform Internals - Technical Architect

Key Responsibilities

• AI Systems Architecture Design and evolve large-scale AI systems that support ChatGPT, OpenAI API, Codex, agentic workflows, multimodal models, and research workloads.
• Define architecture across inference runtime, model serving, request routing, batching, KV-cache handling, GPU scheduling, distributed execution, observability, release gates, and production rollout.
• Own technical trade-offs across latency, throughput, reliability, correctness, safety, scalability, cost, and infrastructure efficiency.
• Inference Runtime & Model Serving Architect high-throughput, low-latency inference systems across large-scale GPU clusters.
• Work across inference engines, serving layers, scheduling systems, caching, streaming, deployment pipelines, and runtime optimization.
• Partner with engineering teams to improve model-serving efficiency, tail latency, GPU utilization, memory efficiency, correctness under load, and cost per request.
• Guide architecture decisions involving PyTorch, JAX, Triton, vLLM-style serving, CUDA/Triton kernels, distributed inference, tensor parallelism, pipeline parallelism, model sharding, and long-context serving.
• GPU, Kernel & Distributed Performance Analyze and improve performance across GPU kernels, memory movement, collective communication, orchestration, and runtime scheduling.
• Identify system-level bottlenecks across compute, memory, networking, scheduling, model execution, and data movement.
• Context Engineering Design and guide context engineering frameworks that determine what information should be passed to the model, how it should be structured, how much context should be used, and how context quality should be measured.
• Create architecture patterns for prompt structure, dynamic context assembly, retrieval-augmented generation, long-context management, conversation memory, tool context, agent state, multimodal context, source grounding, permission-aware retrieval, context compression, and context auditability.
• Ensure AI systems use the right context, from the right source, with the right permissions, at the right cost, and with measurable quality.
• Cost Optimization Frameworks Design and build cost optimization frameworks for large-scale LLM and GenAI workloads.
• Create architecture patterns that reduce unnecessary token usage, redundant retrieval, repeated model calls, inefficient inference paths, and avoidable infrastructure spend.
• Drive model routing, token budgeting, prompt compression, context pruning, semantic caching, response caching, batch inference, async execution, fallback strategies, and cost telemetry across AI workflows.
• Support frontier model workflows across pre-training, post-training, reinforcement learning, agent training, evaluation harnesses, and large-scale experiment execution.
• Release Safety, Validation & Evaluation Gates Architect validation and release systems that ensure model updates, inference engine changes, runtime images, prompt changes, context changes, and platform releases are correct, safe, performant, and regression-free.
• Define release gates across correctness, numerical stability, latency, throughput, token usage, cost regression, context quality, retrieval quality, safety behavior, reliability, and model output quality.
• Design systems that make AI infrastructure observable, debuggable, reliable, and operationally safe.
• Turn production issues into stronger platform abstractions, safer rollout mechanisms, better automation, and more reliable infrastructure.
• Agentic & Multimodal Platform Internals Support architecture for AI agents, tool use, memory, function calling, multimodal interaction, long-running workflows, and internal or external agent deployment.
• Work across agent harnesses, evaluation pipelines, workflow orchestration, safety controls, state management, tool execution, memory systems, and product-facing runtime constraints.
• Ensure agentic and multimodal systems are reliable, observable, secure, cost-aware, and safe under real workloads.

Requirements

• 10-12 years of experience in software engineering, systems architecture, ML infrastructure, distributed systems, platform engineering, inference systems, cloud infrastructure, or large-scale backend engineering.
• Strong hands-on engineering experience with Python and at least one systems/backend language such as C++, Go, Rust, Java, or TypeScript.
• Deep understanding of distributed systems, production infrastructure, reliability engineering, scalability, observability, and fault-tolerant architecture.
• Experience designing or operating large-scale systems involving APIs, microservices, distributed compute, orchestration, job scheduling, caching, high-availability infrastructure, and production monitoring.
• Practical understanding of GPU systems, accelerator-based workloads, CUDA/Triton-style programming, distributed inference, GPU profiling, memory optimization, and communication libraries such as NCCL or RCCL.
• Experience with ML frameworks and serving stacks such as PyTorch, JAX, TensorFlow, Triton, vLLM-style serving, Apache Ray, Kubernetes-based serving, or internal model-serving systems.
• Strong communication skills with the ability to write clear architecture documents, evaluate trade-offs, review implementation quality, and align teams around technically sound decisions.