Lead Software Architect

NeuReality is seeking a Lead System Architect to join our system architecture team and help define NR-NEXUS, our next-generation AI inference platform.

Responsibilitie

• sLead the software architecture and technical roadmap for NeuReality’s NR-Nexu
• sWrite system specifications for NR-Nexus produc
• tResearch AI infrastructure, SaaS platforms, model serving, and inference trend
• sWork with engineering to translate technical capabilities into product valu
• eWork closely with engineering teams to optimize performance, scalability, and feature delivery
• .Define performance goals and lead profiling, benchmarking, and optimization efforts for GenAI and distributed AI workloads
• .Collaborate with customers, partners, and open-source communities to ensure ecosystem compatibility and adoption
• .Mentor software engineers and provide technical leadershi

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Requirement

• s:7+ years of software engineering experience, including 3+ years in software architecture or technical leadershi
• p.Strong experience with Kubernetes-based platforms and cloud-native architectur
• e.Deep understanding of Gen AI/LLM infrastructure and distributed workloa
• dsExperience designing management software or SaaS platforms for production system
• s.Strong background in distributed systems, microservices, APIs, and automatio
• n.Hands-on experience with observability stacks, monitoring, logging, alerting, and SLA/SLO trackin
• g.Experience with CI/CD, deployment automation, upgrades, and rollback mechanism
• s.Good understanding of security, authentication, authorization, and integration with customer data center environment

s.
Nice to h

• aveDeep understanding of GenAI / LLM inference infrastructure, including model serving, scaling, batching, latency, throughput, and resource utilizati
• on.Experience with production AI inference clusters using GPUs, AI accelerators, or other specialized compute infrastructu
• re.Understanding of how distributed inference systems operate, including scheduling, load balancing, autoscaling, failover, and cluster-level observabili
• ty.Experience with LLM serving frameworks such as vLLM, Triton Inference Server, TensorRT-LLM, or simil
• ar.Familiarity with GPU/accelerator orchestration, device plugins, resource scheduling, and cluster capacity planni
• ng.Familiarity with GPU communication technologies such as GPUDirect RDMA, NCCL, NVLink, or UALi
• nk.Experience optimizing communication for distributed AI/ML workloa
• ds.Knowledge of Prometheus, Grafana, OpenTelemetry, Helm, Argo CD, Istio, KServe, Kubeflow, or similar too
• ls.Experience deploying software in on-prem, edge, private cloud, or hybrid environmen