Principal AI Platform Engineer (Remote)

Serko; Seattle (WA); On-site; USD 168,000 - 230,000; Full time

Benefits offered by this job

Competitive base pay

Medical benefits

Discretionary incentive plan

Learning & development platform

Flexible work policy

Pay range: $168,000-$230,000

Job summary

Serko in the United States seeks a Principal Engineer to own the technical vision for our AI Platform and Operations. This remote role can be based in Washington or California, reporting to the AI Platform leadership.

You will architect, scale, and monitor ML infrastructure, mentor engineers, and set standards for deployment, observability, and reliability while partnering with product teams to power the company's AI initiatives.

Qualifications

• 12+ years of engineering experience, with at least 4 years at a Principal or Distinguished level.
• Expert-level knowledge of model serving (e.g., Triton, vLLM, Ray Serve) and cloud/Kubernetes.
• Proven experience with ML tooling and MLOps ecosystems (MLflow, W&B, Kubeflow).
• Strong Python skills and systems-thinking in Docker/Kubernetes environments.
• Hands-on experience with LLM inference at scale and throughput-latency trade-offs.

Responsibilities

• Architect the long-term technical roadmap for the AI platform including model serving and feature stores.
• Set engineering benchmarks for deployment, versioning, A/B testing, and rollbacks.
• Lead GPU/compute strategies and cost optimization for ML workloads at scale.
• Design observability, monitoring, and alerting for AI production systems.
• Mentor senior engineers, lead architecture reviews, and evaluate cloud services for ML.

Skills

Senior expertise

ML infrastructure

Kubernetes

Python

LLM inference

Tools

Triton

vLLM

Ray Serve

MLflow

Weights & Biases

Kubeflow

Job description

Serko in the United States seeks a Principal Engineer to own the technical vision for our AI Platform and Operations. This remote role can be based in Washington or California, reporting to the AI Platform leadership.

You will architect, scale, and monitor ML infrastructure, mentor engineers, and set standards for deployment, observability, and reliability while partnering with product teams to power the company's AI initiatives.