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# Senior AI Engineer in Chicago

- **Company:** Energy Jobline
- **Location:** Chicago, IL, United States
- **Experience:** Expert
- **Salary:** $200,000.0 - $300,000.0
- **Contract:** Permanent contract
- **Skills:** Training Data, Application Programming Interfaces (APIs), Artificial Intelligence, Python (Programming Language), Regression Testing, Software Engineering, Large Language Models, Model Validation, Rate Limiting, Kubernetes, Low Latency, HuggingFace, Hardware Infrastructure
- **Published:** September 9, 2026
- **Apply:** https://www.energyjobline.com/job/senior-ai-engineer-chicago-31579940

## About the Role

* 5+ years software engineering; strong Python
 * Production fine-tuning or distillation of open-weight models (not just inference API wrappers)
 * Experience serving LLMs on-prem (vLLM, TGI, Triton, or equivalent)
 * Experience managing GPU infrastructure (provisioning, scheduling, utilization monitoring) in a production environment
 * Model evaluation and regression testing in production
 * Kubernetes and GPU workload management
 * Strong grasp of the tradeoffs between open and closed models across cost, quality, latency, and data sensitivity

:

 * Quantization, PEFT/LoRA, or other efficient training techniques
 * Model gateway or inference proxy design (routing, fallback, rate limiting)
 * Financial services or other regulated/sensitive-data environments
 * Familiarity with the open model ecosystem (Hugging Face, model cards, licensing

## Description

* Build and operate a model gateway routing inference across open and closed models with cost, latency, and quality tracking
 * Design and run distillation pipelines: use frontier model outputs to generate training data for task-specific open models
 * Fine-tune and evaluate open-weight models (Llama, Qwen, Mistral, or similar) for DV-specific tasks
 * Deploy and maintain on-prem inference infrastructure (vLLM, TGI, or equivalent) on KubernetesBuild model evaluation frameworks for quality, cost, latency, and regression
 * Define criteria and tooling for model selection: when open models are production-ready vs. when to use closed APIs
 * Partner with the agent engineering team to ensure the model layer meets agent workload


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