Professional Resume Template for
LLM Engineer
Sylvia R. Montgomery
Seattle, WA
(206) 555-0143
sylvia.montgomery@ai-mail.com
linkedin.com/in/sylvia-montgomery-ai | github.com/sylviamontgomery-ai | sylvia-ai.dev
Professional Summary
Methodical LLM Engineer with 5 years of experience building, optimizing, and deploying generative AI systems and large language models within enterprise technology environments. Expertise includes orchestrating Retrieval-Augmented Generation (RAG) pipelines, agentic workflows, fine-tuning open-source models, and managing LLM lifecycle observability. Successfully reduced model inference costs by 35% and decreased latency by 28% through custom quantization and caching strategies. Proficient with Python, PyTorch, LangChain, LlamaIndex, Hugging Face, Qdrant, Triton, and AWS SageMaker.
Work Experience
LLM Engineer — Apex Cognitive Systems
Seattle, WA | January 2023 – Present
- Orchestrated a production Retrieval-Augmented Generation (RAG) pipeline using Qdrant vector database and LangChain, reducing response hallucination rates by 42% across 3 enterprise applications.
- Fine-tuned open-source Llama 3 models using QLoRA and Hugging Face PEFT libraries, improving domain-specific summarization accuracy by 26% while reducing model size by 60%.
- Deployed Triton Inference Server for model serving and optimized GPU allocation, boosting inference throughput by 55% and saving $14,000 monthly in cloud infrastructure expenses.
- Established an automated LLM evaluation suite using Ragas and LangSmith, reducing the QA cycle time for prompt adjustments from 5 business days to under 4 hours.
Machine Learning Engineer — Vortex Software Solutions
Bellevue, WA | September 2021 – December 2022
- Developed and maintained Python-based ETL pipelines utilizing Apache Spark and pandas to process 12TB of unstructured textual data for model training datasets.
- Built and deployed a random forest classifier for customer churn prediction in AWS SageMaker, increasing prediction recall by 18% and retaining $85,000 in annual recurring revenue.
- Implemented MLflow for experiment tracking and model registry across 4 engineering teams, reducing production deployment times for new ML models from 3 weeks to 2 business days.
- Automated model performance drift monitoring using Prometheus and Grafana, lowering the average time-to-remediate production model degradation by 38%.
Education
Bachelor of Science in Computer Science
University of Washington · Seattle, WA · 2021
Skills
Large language models (LLMs), Generative AI, Retrieval-Augmented Generation (RAG), Fine-tuning (LoRA/QLoRA), Prompt engineering, Vector databases (Qdrant, Pinecone), LangChain, LlamaIndex, PyTorch, Python, Hugging Face, MLflow, Triton Inference Server, AWS SageMaker, Docker, Kubernetes, Prometheus, Grafana
Projects
Enterprise Knowledge Base Chatbot
Role: Lead AI Engineer
Tools: Python, LangChain, Qdrant, OpenAI API, Streamlit, Docker
Architected and deployed a multi-source RAG system querying 50,000+ internal documents, reducing customer support ticket volume by 32% and achieving a 91% user satisfaction rating.
Open-Source LLM Quantization & Deployment
Role: LLM Engineer
Tools: PyTorch, Hugging Face PEFT, Triton Inference Server, CUDA, Kubernetes
Quantized and optimized Llama-based models to 4-bit precision, reducing GPU memory footprint by 58% and enabling local deployment on cost-effective hardware configurations.
Certifications
- Microsoft Certified: Azure AI Engineer Associate (2025)
- Google Cloud Professional Machine Learning Engineer (2024)
- NVIDIA Certified Associate: Generative AI & Large Language Models (2025)
Additional information
- Languages: English (Native), Mandarin (Conversational)
- Open Source: Contributor to LangChain and Hugging Face repositories
- Availability: 2 weeks notice
Job Market Insights
Market data and opportunities for
LLM Engineer
Job Market Insights
$165,000
-
$240,000
Avg:
$202,000
Growth Outlook:
The demand for specialized AI and LLM Engineers in the United States is growing rapidly, driven by enterprise-wide integration of generative AI. While the Bureau of Labor Statistics does not yet track LLM Engineers separately, the broader category of Computer and Information Research Scientists is projected to grow by 20% from 2024 to 2034. As companies transition from experimental pilots to production-grade deployments, engineers skilled in LLMOps, RAG architectures, and fine-tuning will continue to see strong hiring demand across technology, finance, and healthcare sectors.
20% growth over 10 years (2024–2034)
Key Skills Required
Focus on these skills when customizing your resume for recruiter screenings.
Search Jobs
Explore live openings for
LLM Engineer
roles and tailor your resume to match the market demand.
Search
LLM Engineer
FAQ
Common questions about the
LLM Engineer
position