Network Infrastructure & AI Engineer

Pairwise•Menlo Park, CA

1d•Hybrid

About The Position

At Pairwise, we are a team of engineers, operators, and builders focused on enabling the next generation of technical talent to do their best work. We partner with leading technology companies to deliver high-performance engineering solutions across embedded systems, networking, semiconductors, and software. Our teams have contributed to some of the most advanced products in consumer, infrastructure, and communications technologies. This full-time role offers the chance to work on-site with a global leader in the technology industry, contributing to impactful, technically challenging projects in a high-caliber and fast-moving environment.

Requirements

BS in Computer Science, Electrical Engineering, or a related field with 5+ years of relevant experience; or MS with 2+ years; or PhD with no prior industry experience required.
Hands-on experience with Azure and AWS networking primitives (VNets/VPCs, peering, private endpoints, hybrid connectivity).
Strong Python skills for AI application development.
Production experience with infrastructure-as-code (Terraform, Bicep), Kubernetes, and CI/CD for both infra and ML workloads.
Experience building or operating RAG and/or agentic systems, with exposure to LangGraph, LangChain, or comparable frameworks.
Demonstrated ability to operate independently in ambiguous environments - scoping vague problems, sequencing the work, and driving to a result without close direction.

Nice To Haves

Direct exposure to the telecom domain: mobile network operators, RAN/core architecture, 4G/5G, or working with OEM network equipment vendors.
Experience designing systems under strict security and compliance constraints.
Familiarity with observability for LLM applications: tracing, evals-as-CI, and cost/latency monitoring.

Responsibilities

Design and operate cloud network infrastructure on Azure and AWS, including VNets/VPCs, peering, private connectivity, and segmentation.
Own the infrastructure-as-code, Kubernetes, and CI/CD pipelines that deliver both our network footprint and our ML workloads.
Build and run the platform that hosts our AI workloads: model gateways, vector stores, ingestion pipelines, and deployment plumbing.
Develop RAG workflows end-to-end and implement agentic features using LangGraph and LangChain.
Build and run evaluations for AI quality so changes ship with confidence rather than vibes.