Archived listing. The details below describe a past opening.
Cloud Engineer II This Statement of Work covers a six-week Phase 0 proof-of-concept engagement. Phase 0 is intended to validate feasibility, not to deliver an enterprise-scale production service. This position will deploy an open-weight model endpoint in the client's infrastructure in AWS, connect it to OpenCode, and provide enough evidence for the client to decide whether to proceed to Phase 1. The Phase 0 business case is based on validating whether open-weight inference can work for non-Opus coding workloads, with success criteria including model quality, latency, throughput, end-to-end developer flow, developer satisfaction, and a go/no-go decision for the next phase.
Responsibilities
- Deploy and configure an AWS-hosted, open-weight coding model endpoint using client-provided infrastructure.
- Configure model-serving runtime, GPU allocation, endpoint exposure, health checks, service routing, basic error logging, credential handling, and repeatable deployment artifacts.
- Expose the model endpoint through an OpenAI-compatible API path capable of supporting representative coding prompts.
- Configure and validate OpenCode integration with the model endpoint, including any required adapter, proxy, gateway, or compatibility layer.
- Demonstrate end-to-end OpenCode-to-model request flow for representative coding-agent workflows.
- Demonstrate representative coding-assistance use cases, including code completion, code explanation, unit test generation, refactoring assistance, error diagnosis, small feature implementation, tool usage, repository/file-context prompts where supported prompts and agent skills, and OpenCode/Open Spec workflows.
- Collect and save basic performance data in Grafana, including time to first token, end-to-end latency, tokens per second, concurrency behavior, error rate, GPU utilization, and memory utilization.
- Implement baseline observability for the POC, including logs, health checks, basic metrics, and simple dashboard or metric views where feasible.
- Provide reusable deployment and configuration artifacts, such as Helm values, Kubernetes manifests, scripts, runtime configuration, gateway/proxy configuration, runbook, troubleshooting notes, and environment assumptions.
- Support limited developer validation with a small client-designated pilot group if access, timing, and dependencies allow.
- Support a lightweight quality comparison between the open-weight model and client's current coding-assistant experience if time and access allow.
- Provide preliminary Phase 1 sizing inputs, including throughput observations, bottlenecks, latency drivers, scaling considerations, recommended AWS instance families, and remaining unknowns.
- Deliver a written final report covering architecture, deployment, OpenCode integration results, functional test results, performance measurements, technical gaps, security and operational considerations, cost and scale observations, reusable artifacts, and Phase 1 readiness.
- Deliver a clear Phase 1 go/no-go recommendation, including required changes, unresolved risks, and recommended next steps for AWS-based enterprise-scale implementation.
- Conduct a knowledge-transfer session with stakeholders at the end of the engagement.
Required Qualifications
- 3-5+ years of Cloud Engineering experience.
- Hands-on AWS, Kubernetes, Docker, and API integration experience.
- Experience with infrastructure automation (Terraform, Helm, Kubernetes manifests, or similar).
- Strong experience with monitoring and observability tools such as Grafana and CloudWatch.
- Ability to troubleshoot distributed cloud environments and performance issues.
Preferred Qualifications
- Experience with Generative AI, LLMs, or AI infrastructure.
- Familiarity with model-serving platforms such as vLLM, Triton, TGI, or similar.
- Experience supporting GPU-based workloads in AWS.
- Knowledge of OpenAI-compatible APIs and AI-powered developer tools.