The MLOps Engineer owns the model lifecycle across all defence and strategic sector deployments — from serving infrastructure and monitoring to evaluation pipelines and environment management. You will work across both layers: supporting Strategic Deployment Engineers in the field, and owning the model deployment infrastructure for new products being built by the product engineering team.
Design and operate model serving infrastructure across on-prem and cloud deployments.
Build and maintain CI/CD pipelines for model updates, rollbacks, and evaluation-gated deployments.
Monitor model performance in production — latency, accuracy drift, throughput, failure modes — and build systems that surface issues before clients do.
Build evaluation infrastructure: harnesses, A/B testing, and model comparison tooling for field and lab use.
Deep expertise in model serving: vLLM, TGI, Triton Inference Server, or equivalent; experience with quantised model formats (GGUF, AWQ, GPTQ).
Experience fine-tuning and adapting models in constrained, on-prem, or air-gapped environments, including managing data pipelines and compute limitations specific to the environment.
Containerisation experience with Docker, Kubernetes, or lightweight alternatives (K3s, K0s) for constrained and edge environments; familiarity with deploying across heterogeneous hardware and infrastructure configurations.
Monitoring and observability using Prometheus, Grafana, or equivalent; ability to build custom eval dashboards.
Experience: 3–5 years · Job type: Full-time
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San Jose, California, United StatesNorth America
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