Supervised Fine-Tuning; SFT/Site Reliability Engineer; SRE) OmegaHires · Phoenix · Site Reliability not stated
Listed on 2026-10-22
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IT/Tech
SRE/Site Reliability, Cloud Computing: Infrastructure & Operations
Mid-level Site Reliability role enix
· contract.
devopsroles summary, based on the employer's posting.
What you'll do- Prepare and validate training data, run LLM fine-tuning, then assess model results
- Keep AI applications and infrastructure available and performing reliably
- Use Python and scripts to automate deployments, monitoring and operational work
- Investigate production failures, find root causes and strengthen system stability
- Support cloud infrastructure, CI/CD pipelines, containers and observability tools
- You have used Python and automation in production systems
- For SFT work, you know LLMs, fine-tuning processes, training data and model evaluation
- For SRE work, you bring experience with cloud platforms, Linux, CI/CD, containers, monitoring and incident response
- You can troubleshoot issues, analyze problems and communicate clearly
This role suits an engineer whose experience is in AI model fine-tuning, site reliability, or both. It is a contractor position based in Phoenix.
From the employerSupervised Fine-Tuning (SFT) / Site Reliability Engineer (SRE)
Job
Summary:
We are seeking an engineer with experience in AI model fine-tuning and/or Site Reliability Engineering to support AI systems, infrastructure reliability, and production operations.
Key Responsibilities:
- Support supervised fine-tuning (SFT) of LLMs, including dataset preparation, data quality validation, training, and model evaluation.
- Monitor and maintain the reliability, availability, and performance of AI applications and infrastructure.
- Automate deployment, monitoring, and operational tasks using Python and scripting tools.
- Troubleshoot production issues, perform root-cause analysis, and improve system stability.
- Support cloud infrastructure, CI/CD pipelines, containers, and observability tools.
Required Skills:
- Experience with Python, automation, and production systems.
- For SFT: familiarity with LLMs, fine-tuning workflows, training datasets, and model evaluation.
- For SRE: experience with cloud platforms (AWS/Azure/GCP), Linux, CI/CD, Docker/Kubernetes, monitoring, and incident management.
- Strong troubleshooting, analytical, and communication skills.
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