Job Summary The Senior LLM Engineer will fine-tune and deploy LLMs on Azure OpenAI / Azure AI Foundry to power generative AI capabilities within our engineering design platform, translating natural-language design intent into structured, engineering-valid outputs.
Responsibilities
- Fine-tune foundation models (LoRA/QLoRA, RLHF/DPO, instruction tuning) for domain-specific tasks and terminology.
- Build agentic and tool-use workflows that connect the LLM to internal engineering tools and data sources.
- Design evaluation harnesses for factuality and accuracy; own hallucination and safety guardrails.
- Build RAG pipelines (Azure AI Search) and structured tool-use/function-calling systems on top of fine-tuned Azure OpenAI models.
- Deploy and optimize inference (quantization, KV-cache, vLLM/TensorRT-LLM) on Azure Kubernetes Service for cost-efficient serving.
Qualifications
- 8+ years overall ML/AI engineering experience, with deep hands-on LLM/foundation model work not just calling APIs.
- Demonstrated experience fine-tuning models (LoRA/QLoRA/PEFT, full fine-tuning, or pretraining at some scale).
- Practical experience with alignment techniques (RLHF, RLAIF, DPO, or instruction tuning).
- Solid Python and deep PyTorch proficiency; solid grasp of transformer architecture internals.
- Hands-on experience building RAG pipelines, embeddings/vector search, and agentic or tool-use LLM systems.
- Familiarity with Azure OpenAI / Azure AI Foundry or an equivalent cloud LLM platform.
Preferred Qualifications
- Direct experience with Azure OpenAI Service and Azure AI Foundry for enterprise-scale deployment.
- Experience orchestrating LLM-driven code or structured-output generation for technical/engineering domains.
- Distributed training experience across multi-GPU/multi-node clusters (DeepSpeed, FSDP).
- Publications, blog posts, or open-source contributions in generative AI.
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