AI Infrastructure Engineer

Propio • Overland Park, KS

3d

About The Position

At Propio, we don't believe careers happen to people. We believe people create them. Here, you're trusted to make decisions, challenge assumptions, drive innovation, and shape outcomes. Your success is not limited by hierarchy or tenure. It's fueled by your ambition, your curiosity, and your willingness to own your impact. If you're looking for a role where you can simply maintain the status quo, this probably isn't it, but if you're looking for a place where your ideas matter, your growth is accelerated, and your work creates meaningful impact across the world, we'd love to talk.

Every day, communication changes lives. A patient receives care they otherwise couldn't access. A family gains critical information. A business connects with a customer. A community becomes more inclusive. These moments happen because barriers are removed. And behind those moments are Propio team members who show up every day to solve problems, innovate, and build the future. This isn't just work. This is world impact.

As an AI Infrastructure Engineer, you will primarily design, build, and operate our AWS-based, GPU-accelerated inference and streaming serving platform, optimizing it for low latency, high concurrency, reliability, and cost. You will also support the research team’s training environment, further the AI team’s ML/LLMOps capabilities, and enable the development of edge AI deployments. You'll be empowered to:

* Take ownership of important initiatives and outcomes.
* Drive meaningful business results.
* Influence decisions and contribute new ideas.
* Partner with talented, high-performing team members.
* Challenge yourself through continuous learning and growth.
* Help shape the future of a rapidly growing organization

Requirements

• 3+ years of experience building and operating AI/ML infrastructure, inference platforms, or distributed systems in production.
• Hands-on production experience with AWS, particularly EKS, EC2 GPU workloads, ECR, S3, IAM/KMS, VPC networking, and CloudWatch and/or OpenTelemetry.
• Hands-on experience with at least one inference stack, such as vLLM, SGLang, TensorRT-LLM, Triton, KServe, or Ray Serve.
• Experience operating ML/LLM systems in production, including model serving, autoscaling, monitoring, incident response, and performance benchmarking.
• Familiarity with GPU infrastructure and at least one serving stack, such as vLLM, SGLang, TensorRT-LLM, or Triton.
• A working understanding of LLM Ops practices, including evaluation, observability and tracing, cost control, and versioning.

Nice To Haves

• Low-latency, real-time, or streaming inference experience, especially for audio/speech—directly relevant to interpretation.
• Experience with real-time audio pipelines and streaming protocols, including WebRTC, WebSocket, and gRPC streaming.
• Experience supporting distributed training environments using FSDP, DeepSpeed, Megatron-LM, Slurm, EKS, or SageMaker HyperPod.
• Inference optimization experience, including quantization, KV-cache optimization, speculative decoding, and continuous batching.
• Experience building Edge AI deployment toolchains using ONNX Runtime, TensorRT/Jetson, ExecuTorch, llama.cpp, MLC-LLM, or similar runtimes.
• Experience with SRE practices, capacity planning, production incident response, and secure infrastructure for PHI/PII.
• Contributions to relevant open-source infrastructure, serving, observability, or edge-runtime projects.

Responsibilities

• Design, deploy, and operate low-latency streaming inference systems on AWS for LLM, ASR, TTS, and multimodal models.
• Optimize time to first token/audio, p95/p99 end-to-end latency, real-time factor, throughput, concurrency, GPU utilization, and cost per stream using technologies such as vLLM, SGLang, TensorRT-LLM, Triton, TensorRT.
• Support and evolve the research team’s AWS-based training environment, including reproducible containers, GPU job scheduling, distributed job execution, checkpoint and resume capabilities, experiment tracking, model and data artifact access, and researcher self-service.
• Enable workloads using FSDP, DeepSpeed, Megatron-LM, Slurm, EKS, or SageMaker.
• Build model and artifact registries, lineage and versioning, automated evaluation gates, deployment pipelines, shadow and canary releases, rollback workflows, runtime and configuration management, and production observability.
• Partner with researchers and device and embedded teams to build repeatable model optimization, packaging, validation, and deployment workflows for resource-constrained edge targets.
• Support model export and compilation, post-training quantization, runtime integration.
• Own capacity planning, production readiness, incident response, disaster recovery, and the secure operation of the AI platform.
• Apply least-privilege IAM, KMS encryption, private networking, secrets management.

Benefits

• Own Your Growth: We invest in people who invest in themselves. You'll have opportunities to learn, develop, and expand your capabilities while building a meaningful career.
• Own Your Impact: You'll see the connection between your work and our success. We believe great people deserve the opportunity to make a real difference.
• Own Your Innovation: The best ideas can come from anywhere. We encourage curiosity, creativity, and challenging the way things have always been done.
• Own Your Success: Whether you're building expertise, pursuing leadership opportunities, or expanding your career path, we'll give you room to grow and the support to get there.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed