I design, build, deploy, and continuously improve AI products that solve real business problems. My work spans the complete AI product lifecycle—from data preparation and experimentation to scalable APIs, cloud deployment, monitoring, and optimization.

• Generative AI and LLM applications with reliable retrieval, structured outputs, evaluation, and observability
• AI agents and multi-agent workflows for intelligent automation and knowledge-intensive tasks
• Retrieval-augmented generation systems covering ingestion, hybrid search, reranking, and grounded responses
• Computer vision and NLP solutions for classification, detection, segmentation, and text intelligence
• Machine learning prediction systems and recommendation engines designed for production use
• Scalable AI APIs and services with strong integration, performance, and deployment practices
• MLOps pipelines for training, model versioning, monitoring, continuous evaluation, and reliable releases

• GPT, Claude, Llama, Mistral, Gemini, and fine-tuned models
• LangChain, LangGraph, LlamaIndex, CrewAI, and AutoGen
• Prompt engineering, function calling, structured outputs, and tool orchestration
• MCP, agent memory, multi-agent systems, and human-in-the-loop workflows
• RAG, embeddings, vector search, hybrid retrieval, reranking, and document processing
• Ragas, LLM-as-a-Judge, hallucination detection, retrieval metrics, and response evaluation
• LoRA, QLoRA, PEFT, parameter-efficient fine-tuning, and domain adaptation

• Scikit-learn, XGBoost, LightGBM, CatBoost, Optuna, and Hyperopt
• PyTorch, TensorFlow, Keras, ONNX Runtime, TorchVision, and Detectron2
• OpenCV, YOLO, object detection, image classification, and segmentation
• Hugging Face Transformers, spaCy, NLTK, sentence transformers, and Whisper
• Diffusion models, Stable Diffusion, GANs, VAEs, and generative media
• MLflow, Weights & Biases, TensorBoard, experiment tracking, and model evaluation

• Python, JavaScript, TypeScript, R, SQL, and Bash
• NumPy, pandas, Polars, SciPy, Matplotlib, Seaborn, and Plotly
• Apache Spark, PySpark, Kafka, Airflow, dbt, and event-driven pipelines
• PostgreSQL, MySQL, MongoDB, Redis, Elasticsearch, and cloud data warehouses
• Snowflake, BigQuery, Redshift, and Databricks
• Feature engineering, data validation, ETL, schema management, and reproducible workflows

• FastAPI, Flask, Django REST Framework, REST, GraphQL, gRPC, and WebSockets
• Docker, Kubernetes, Helm, GitHub Actions, CI/CD, GitOps, and Infrastructure as Code
• AWS, Microsoft Azure, and Google Cloud Platform
• TensorFlow Serving, TorchServe, Triton, vLLM, and serverless AI
• API gateways, load balancing, autoscaling, monitoring, observability, and A/B testing

Pinecone · Weaviate · Milvus · Qdrant · Chroma · FAISS · pgvector

I treat AI engineering as a production discipline, not just a model-building exercise.

• Start with measurable business and technical outcomes
• Build strong data foundations before adding complexity
• Establish baselines and evaluate quality with the right metrics
• Combine deterministic software engineering with probabilistic AI behavior
• Design for scale, reliability, cost, latency, security, and observability
• Monitor models and AI applications continuously after deployment
• Keep systems maintainable through clean APIs, testing, documentation, and ownership

Good AI systems create measurable value, earn user trust, and remain dependable as data and requirements change.

Explore my repositories to see my latest projects, experiments, and open-source work:

👉 github.com/rexShail?tab=repositories

I'm interested in challenging AI engineering work, meaningful collaborations, and conversations about building reliable AI systems.