07/15/2026 updated
100 % available
Senior AI Engineer | LLMs & Generative AI | MLOps | Computer Vision
Amarillo, USA
Only remote
M.S. Computer Science, Machine LearningAbout me
Deep expertise in large language models and generative AI, computer vision, and end-to-end MLOps. Combines strong research instincts with production discipline — shipping models that are accurate, monitored, and cost-efficient at scale — and mentors engineering teams to do the same.
Artificial IntelligenceComputer VisionBig DataMachine LearningNatural Language ProcessingOpenCVTensorFlowAzure Machine LearningPyTorchLarge Language ModelsApache SparkDeep LearningGenerative AIMachine Learning OperationsTerraform
LLMs & Generative AI
Expertise in large language models and generative AI, including Transformers, Hugging Face, LangChain, LlamaIndex, Retrieval-Augmented Generation (RAG), fine-tuning with LoRA / QLoRA / PEFT, RLHF, prompt engineering, vLLM, and OpenAI & Anthropic APIs.
ML / Deep Learning
Proficiency in machine learning and deep learning frameworks including PyTorch, TensorFlow, JAX, Keras, scikit-learn, XGBoost, LightGBM, and CatBoost for designing and deploying production-grade models.
MLOps & Deployment
Hands-on experience with MLflow, Kubeflow, Weights & Biases, BentoML, NVIDIA Triton, Docker, Kubernetes, Terraform, GitHub Actions CI/CD, model registries, and drift & performance monitoring for end-to-end MLOps pipelines.
Computer Vision
Application of computer vision techniques using OpenCV, YOLOv8, Detectron2, CNNs, image segmentation, object detection, and OCR for real-world production systems.
Natural Language Processing
NLP capabilities covering spaCy, NLTK, sentence embeddings, named-entity recognition, text classification, summarization, and semantic search.
Cloud & Big Data
Experience with cloud and big data platforms including AWS (SageMaker, Bedrock, EC2 GPU, Lambda, S3), GCP Vertex AI, Azure ML, Apache Spark, Kafka, Databricks, Snowflake, and Airflow.
Vector Stores
Knowledge of vector store technologies including Pinecone, Weaviate, FAISS, Milvus, and pgvector for building scalable retrieval systems.
Programming Languages & Tools
Proficiency in Python, SQL, Scala, C++, TypeScript, Bash, and Git for developing and maintaining AI and data engineering solutions.
Specialties
Capabilities in distributed and multi-GPU training, model quantization and inference optimization, feature stores, experiment design and A/B testing, and responsible AI and evaluation.
Expertise in large language models and generative AI, including Transformers, Hugging Face, LangChain, LlamaIndex, Retrieval-Augmented Generation (RAG), fine-tuning with LoRA / QLoRA / PEFT, RLHF, prompt engineering, vLLM, and OpenAI & Anthropic APIs.
ML / Deep Learning
Proficiency in machine learning and deep learning frameworks including PyTorch, TensorFlow, JAX, Keras, scikit-learn, XGBoost, LightGBM, and CatBoost for designing and deploying production-grade models.
MLOps & Deployment
Hands-on experience with MLflow, Kubeflow, Weights & Biases, BentoML, NVIDIA Triton, Docker, Kubernetes, Terraform, GitHub Actions CI/CD, model registries, and drift & performance monitoring for end-to-end MLOps pipelines.
Computer Vision
Application of computer vision techniques using OpenCV, YOLOv8, Detectron2, CNNs, image segmentation, object detection, and OCR for real-world production systems.
Natural Language Processing
NLP capabilities covering spaCy, NLTK, sentence embeddings, named-entity recognition, text classification, summarization, and semantic search.
Cloud & Big Data
Experience with cloud and big data platforms including AWS (SageMaker, Bedrock, EC2 GPU, Lambda, S3), GCP Vertex AI, Azure ML, Apache Spark, Kafka, Databricks, Snowflake, and Airflow.
Vector Stores
Knowledge of vector store technologies including Pinecone, Weaviate, FAISS, Milvus, and pgvector for building scalable retrieval systems.
Programming Languages & Tools
Proficiency in Python, SQL, Scala, C++, TypeScript, Bash, and Git for developing and maintaining AI and data engineering solutions.
Specialties
Capabilities in distributed and multi-GPU training, model quantization and inference optimization, feature stores, experiment design and A/B testing, and responsible AI and evaluation.
Languages
EnglishNative speaker
Project history
Leading a team of 5 ML engineers delivering production AI across manufacturing and supply chain, owning the full model lifecycle from research through deployment and monitoring. Architected a computer-vision defect-detection system (PyTorch, YOLOv8, Detectron2) for assembly-line inspection, reaching 98.6% detection accuracy and cutting manual QA time by 65%. Built a production RAG assistant over 40,000+ engineering and maintenance documents (LlamaIndex + pgvector + a fine-tuned open-weight LLM), reducing technician lookup time from ~25 minutes to under 2. Fine-tuned domain LLMs with LoRA / QLoRA on proprietary corpora and served them via vLLM, improving task accuracy 18% over off-the-shelf baselines while cutting inference cost 40%. Standardized an MLOps platform on AWS SageMaker with MLflow, Kubernetes, and CI/CD, reducing time-to-production for 20+ models from weeks to days.
Developed deep-learning load-forecasting models (LSTM, Temporal Fusion Transformer) that cut day-ahead forecast error (MAPE) from 6.2% to 3.1%, improving grid dispatch and lowering reserve costs. Built an anomaly-detection system over 8 TB/day of smart-meter data (autoencoders + isolation forests) to flag equipment faults and energy theft, recovering an estimated $1.4M annually. Deployed and monitored models with SageMaker endpoints, automated Airflow retraining, and Weights & Biases experiment tracking.
Built gradient-boosted fraud-detection models (XGBoost) on streaming transaction data, increasing recall 22% while reducing false positives 35%. Developed an NLP pipeline (spaCy) to classify and route customer inquiries, automating ~40% of first-line triage. Engineered a feature store and automated scoring pipelines powering credit-risk and customer-churn models.