Your opportunity

Our client is a well-funded, seed-stage AI startup that builds agents for the factory floor. They develop and distribute a software-first agent layer that plugs into the cameras and machines factories already have. Their models run and act at the edge so agents can see, decide, and act in real time. Events and metrics flow into a dashboard that provides plant teams immediate visibility. They’re approaching a large (~$14B) and underserved market with a disruptive, asset-light alternative to hardware-heavy robotics and batch analytics and they’ve already found early traction with clients in the food & beverage, pharma/cosmetics, and materials processing verticals.

As a staff infrastructure engineer, you’ll join an emergent platform team and help shape the form that it takes. You will become fluent in the hardware platform, networking topologies, and application stack and draw on your longitudinal perspective to build an infrastructure engineering practice and guide the leadership team’s decision making. You’ll be firmly in the critical path (to begin) as the primary on-call and equipped to turn incidents into monitoring signals, build playbooks from first principles, and shape a culture of streamlined root cause analysis.

You’ll be joining a flat, dynamic environment in the midst of its scale-up phase that’s led by an accomplished ex-Deepmind researcher with specialization in reinforcement learning, deep learning and robotics. The company closed a $13.9M CAD seed round in March of 2025 and are scaling R&D and delivery to meet accelerating demand, with headcount tracking to double by year-end.

Please note that this role may involve participation in an on-call rotation that includes evenings and weekends.

Key responsibilities 

• Infrastructure & application ownership: Design and implement scalable infrastructure architectures across on-premise (edge) and cloud environments; evolve core infrastructure platforms that support production and pre-production workflows

• Pre-production environments & validation: Build and maintain sandbox, staging, and shadow-run environments that mirror production behavior; own how systems are provisioned, isolated, tested, and validated before rollout

• Replay-based testing & safe version rollouts: Design infrastructure to support A/B playback testing of models and software versions, offline and replay-based workload testing, and shadow-mode execution prior to version switching

• Reliability engineering, fault isolation & performance determinism: Define infrastructure standards that ensure reliable, isolated systems with predictable performance under real-world workloads

• Operability & cross-team collaboration: Partner with DevOps to ensure infrastructure designs are deployable, observable, and operable; collaborate with Edge and AI teams to enable safe experimentation

Tech stack

• Operating system: Linux

• Backend: Python (Flask, FastAPI), TypeScript/Node.js

• Orchestration & compute: Kubernetes, on-prem bare metal, VMs

• Containers: Docker

• Monitoring, observability & logging: Prometheus, Grafana, ELK

• Cloud providers: AWS, Azure, GCP

• Databases & storage: SQL, InfluxDB, MongoDB

• Messaging & IoT: MQTT, HTTP/REST, RabbitMQ, Apache Kafka

• Edge platforms: NVIDIA Jetson, Raspberry Pi (ARM)

• GPU/acceleration: CUDA, TensorRT, ONNX, OpenVINO

• ML/DL frameworks: PyTorch, TensorFlow, Keras, scikit-learn

• Scientific computing: NumPy, Pandas

• Computer vision: OpenCV

• Cameras & vision I/O: GenICam, GigE Vision, USB3 Vision

• Industrial automation: PLC integration; protocols: Ethernet/IP, Modbus, Profinet, OPC UA

Your know-how

• You have significant experience supporting the design and implementation of scaled production environments in hybrid (edge-cloud) or on-prem environments

• You have strong Linux systems knowledge and experience building and operating underlying compute platforms

• You have significant experience with infrastructure orchestration platforms (Kubernetes/K8s preferred) and/or virtualization platforms

• You are experienced with monitoring, observability and alerting stacks and best practices 

• You have high comfort with, and understanding of, distributed systems and failure modes

• You have enough software engineering skills to be dangerous, and specific command of Python for infrastructure automation and validation tooling

• You have experience collaborating effectively within and across cross-functional delivery teams

• You are a contagiously curious person with entrenched learning habits

It’s a bonus if

• You have experience designing and operating scaled production environments for manufacturing, robotics, IoT and/or industrial automation applications

• You have deep expertise in computer vision, robotics, or manufacturing automation

• You have experience supporting GPU-based or real-time workloads

• You are predisposed to mentorship and crafting a culture of continuous improvement

• You have experience scaling an AI and/or B2B SaaS venture

Interested in learning more?

Please upload your resume or a .pdf export of your LinkedIn profile using the following “Apply Now” button, or send your resume or LinkedIn profile URL to talent@lutrapartners.com with “Staff Infrastructure Engineer, Manufacturing AI” as the subject line. One of our talent partners will be in contact shortly.