AI Systems & Platform Internals - Technical Architect

About this role

Accellor is an AI-native services firm purpose-built for the post-ChatGPT era. Free from legacy constraints, we focus on delivering measurable business outcomes through advanced AI, data, and engineering capabilities. Our mission is to operationalize AI at scale and unlock sustained enterprise value. Our offerings span AI solutions, data services, enterprise applications, and product engineering, tailored to industry-specific needs across healthcare, life sciences, telecom, retail, financial services, and technology. By leveraging design thinking and technology-agnostic architectures, we ensure faster time-to-value and seamless interoperability. With a proven track record of enabling Fortune 100 enterprises and global innovators, Accellor stands as a trusted partner for organizations seeking to harness the full potential of AI. Our vision is clear: to build intelligent, connected ecosystems that deliver measurable outcomes and redefine the future of enterprise transformation. Technical Architect — AI Systems & Platform Internals Experience: 10–12 Years Role Type: Technical Architect / Staff-Level Systems Architect Role Summary Accellor is looking for a Technical Architect — AI Systems, Inference & Platform Internals to help design, scale, and optimize the systems that power ChatGPT, OpenAI API, Codex, agentic systems, multimodal experiences, and internal research workloads. This role is focused on the internal AI systems stack, including inference runtime, model serving, GPU infrastructure, distributed systems, context engineering, cost optimization, evaluation gates, observability, release safety, and production reliability. The ideal candidate is a senior hands-on architect who can reason across the full AI platform — from GPU-level performance and distributed inference to product-scale reliability, model deployment, safety, and cost-efficient operations. Key Responsibilities : 1. 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. 2. 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. 3. GPU, Kernel & Distributed Performance Analyze and improve performance across GPU kernels, memory movement, collective communication, orchestration, and runtime scheduling. Guide engineering decisions involving CUDA, Triton, NCCL/RCCL, GPU profiling, memory pressure, compute utilization, tensor layouts, interconnect behavior, and distributed execution. Identify system-level bottlenecks across compute, memory, networking, scheduling, model execution, and data movement. 4. 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. Own 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. 5. 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. Ensure cost optimization does not compromise quality, safety, grounding, reliability, or user experience. 6. Training & Research Infrastructure Collaborate with research and training infrastructure teams to support large-scale model training and post-training workflows. Contribute to architecture around distributed training, checkpointing, orchestration, fault tolerance, observability, data movement, evaluation infrastructure, and experiment velocity. Support frontier model workflows across pre-training, post-training, reinforcement learning, agent training, evaluation harnesses, and large-scale experiment execution. 7. 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. Ensure platform optimizations do not reduce safety, grounding, quality, or user trust. 8. Reliability, Observability & Production Operations Design systems that make AI infrastructure observable, debugg