Professional Resume Template for
AI Engineer
Elena R. Sterling
San Francisco, CA
(415) 555-0142
elena.sterling@email.com
linkedin.com/in/elena-sterling | github.com/elenasterling | elenasterling.ai
Professional Summary
Systems-oriented AI Engineer with 6 years of engineering background designing, fine-tuning, and deploying large language models (LLMs) and Retrieval-Augmented Generation (RAG) systems in cloud environments. Expertise includes optimizing inference pipelines, vector database partitioning, and MLOps automation. Successfully reduced API inference latency by 38% and cut GPU compute costs by 24% using model quantization and continuous batching. Proficient with Python, PyTorch, Hugging Face, Qdrant, Docker, and AWS SageMaker.
Work Experience
AI Engineer — Aetheris AI
San Francisco, CA | March 2023 – Present
- Designed and deployed a Retrieval-Augmented Generation (RAG) system using Qdrant and LlamaIndex for 45,000 active users, reducing support ticket volume by 31% over 4 months.
- Fine-tuned Llama-3-8B using QLoRA on a proprietary dataset of 2.1M tokens, achieving a 94.2% accuracy rate on domain-specific classification tasks.
- Optimized LLM serving pipelines using vLLM and TensorRT-LLM on AWS g5.xlarge instances, reducing p99 API response time by 43% (from 480ms to 273ms).
- Architected a continuous evaluation pipeline using Ragas and GitHub Actions, cutting manual validation efforts by 11 hours per week while improving model reliability by 18%.
Software Engineer (AI/ML) — Vectra Data Systems
San Jose, CA | June 2020 – February 2023
- Engineered a real-time fraud detection pipeline using PyTorch and Kafka, processing 12,000 transactions per second with an average inference latency under 22ms.
- Implemented automated model retraining loops using Kubeflow pipelines, reducing model drift by 19% and saving 14 engineering hours per model release cycle.
- Collaborated with a team of 4 data scientists to transition legacy XGBoost models to cloud endpoints, boosting prediction recall by 12.4%.
- Refactored Docker container builds for ML service microservices, reducing image sizes by 46% and accelerating deploy pipeline speeds by 8 minutes.
Education
Bachelor of Science in Computer Science
San Jose State University · San Jose, CA · 2020
Skills
Python, PyTorch, TensorFlow, Hugging Face, LangChain, LlamaIndex, Qdrant, Pinecone, AWS (SageMaker, EC2, Lambda), Docker, Kubernetes, MLOps, SQL, Git, FastAPI, CI/CD
Projects
Quantized Edge LLM Chatbot
Role: Lead AI Developer
Tools: PyTorch, llama.cpp, C++, Docker
Quantized and optimized 7B-parameter models to run on 8GB RAM devices, achieving 14 tokens/sec generation speed and cutting memory footprint by 58%.
Semantic Search Engine
Role: AI Engineer
Tools: Qdrant, SentenceTransformers, FastAPI, AWS
Built a vector search API indexing 5.2M documents, yielding a 92% search relevance score and servicing 320 queries per second under 85ms latency.
Certifications
- Google Cloud Professional Machine Learning Engineer (2024)
- Microsoft Certified: Azure AI Engineer Associate (2023)
Additional information
- Languages: English (Native), Mandarin (Conversational)
- Volunteer Work: Technical mentor for San Francisco high school robotics clubs (2022-present)
- Availability: 2 weeks notice
Job Market Insights
Market data and opportunities for
AI Engineer
Job Market Insights
$140,000
-
$210,000
Avg:
$175,000
Growth Outlook:
The employment of software developers, quality assurance analysts, and testers—which encompasses AI and Machine Learning Engineers—is projected to grow 15% from 2024 to 2034, much faster than the average for all occupations. The integration of advanced artificial intelligence systems and Retrieval-Augmented Generation (RAG) technologies across traditional software systems continues to drive high demand in tech, finance, and healthcare sectors.
15% growth over 10 years
Key Skills Required
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