Senior LLM Engineer

Job Description: Fine-tune foundation models using LoRA/QLoRA, RLHF/DPO, and instruction tuning for domain-specific tasks and terminology Build agentic and tool-use workflows connecting LLMs to internal engineering tools and data sources Design evaluation harnesses for factuality and accuracy Own hallucination and safety guardrails Build RAG pipelines with Azure AI Search Build structured tool-use and function-calling systems on fine-tuned Azure OpenAI models Deploy and optimize inference using quantization, KV-cache, vLLM, and TensorRT-LLM on Azure Kubernetes Service Help keep Azure AI/ML training and inference workloads reliable, secure, and cost-efficient alongside ML engineers, data scientists, and MLOps teamsRequirements: 8+ years overall ML/AI engineering experience, with deep hands-on LLM/foundation model work Demonstrated experience fine-tuning models using LoRA/QLoRA/PEFT, full fine-tuning, or pretraining at some scale Practical experience with RLHF, RLAIF, DPO, or instruction tuning Strong Python proficiency 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 Direct Azure OpenAI Service and Azure AI Foundry experience preferred Experience orchestrating LLM-driven code or structured-output generation for technical/engineering domains preferred Distributed training experience across multi-GPU/multi-node clusters using DeepSpeed or FSDP preferred Publications, blog posts, or open-source contributions in generative AI preferredBenefits: Variable pay, issued as a monetary bonus or in another form Medical insurance Dental insurance Vision insurance Flexible spending accounts Health savings accounts Life insurance AD&D insurance Disability insurance Retirement benefits Paid vacation/time off Educational assistance Infertility assistance Paid parental leave Adoption assistance Flexible work environment