Job Opportunities API

Your Impact at LILA The Staff/Principal DevOps Engineer - AI Inference will drive the design, implementation, and optimization of infrastructure purpose-built for serving machine learning models at scale. This role bridges platform engineering, site reliability, and ML infrastructure, building the systems that power low-latency, high-throughput inference across GPU clusters and cloud accelerators. You will collaborate with ML engineers, research scientists, and software engineers to build inference platforms that serve models reliably to production users while maximizing compute efficiency. What You'll Be Building GPU/accelerator infrastructure on Kubernetes: scheduling, resource isolation, multi-tenant GPU sharing, device plugins, and topology-aware placement for inference workloads Model serving platforms using frameworks such as vLLM, Triton Inference Server, TGI, or custom serving stacks with optimized batching, caching, and request routing Intelligent request routing and load balancing across heterogeneous accelerator fleets (NVIDIA GPUs, AWS Inferentia/Trainium) to maximize utilization and minimize latency Autoscaling systems that dynamically match inference compute supply with demand across production, research, and experimental workloads Production-grade deployment pipelines for ML models: canary rollouts, A/B testing, model versioning, and safe rollback across multi-region deployments Infrastructure-as-code with Terraform and Helm for GPU-accelerated EKS clusters, including node pools, spot/on-demand strategies, and accelerator-specific networking Observability and performance optimization: GPU utilization monitoring, inference latency profiling, token throughput dashboards, and SLO/SLI tracking for model endpoints CI/CD pipelines for model artifacts: container image builds with CUDA/driver dependencies, model registry integration, and automated inference benchmarking in CI AWS cloud infrastructure for ML: EKS with GPU node groups, EC2 accelerated instances (P4/P5, Inf2, Trn1), S3 model storage, EFA/high-bandwidth networking, and IAM least privilege Cost optimization and capacity planning: right-sizing accelerator instances, spot instance strategies for inference, and fleet-wide efficiency reporting What You'll Need to Succeed Expertise in DevOps, SRE, or Platform Engineering with significant experience operating GPU/accelerator infrastructure at scale Deep experience with Kubernetes for ML workloads: GPU scheduling, resource quotas, node affinity, and accelerator device management Strong proficiency deploying to AWS using infrastructure-as-code (Terraform, Helm) with hands-on experience managing GPU-based compute (EKS, EC2 P-series/Inf/Trn instances) Experience with model serving infrastructure: inference servers, request batching, KV-cache optimization, or LLM serving frameworks Strong understanding of networking for distributed inference: high-bandwidth interconnects, NCCL, VPC/PrivateLink, and load balancing at L4/L7 Strong proficiency in Python for automation, tooling, and integration with ML frameworks Bonus Points For Experience with LLM inference optimization: continuous batching, speculative decoding, quantization (GPTQ, AWQ, FP8), tensor parallelism, and pipeline parallelism Hands-on experience with multiple accelerator families (NVIDIA A100/H100, AWS Inferentia2, Trainium, AMD MI300X) and maintaining hardware-agnostic serving infrastructure Multi-region deployment experience with geographic routing and failover for latency-sensitive inference endpoints Proficiency in Rust or Go for performance-critical infrastructure components SRE practices for ML systems: chaos engineering on GPU workloads, incident management, capacity modeling for bursty inference traffic Experience with model registries, artifact versioning, and ML supply chain security Observability platform expertise: building custom metrics for token-level throughput, time-to-fir