Clayton Smith

Results-driven AI Engineer & Data Scientist with 10+ years of experience architecting and scaling advanced machine learning (ML), deep learning (DL), NLP, computer vision (CV), and generative AI (GenAI) solutions. I specialize in building production-grade, high-impact AI systems, leveraging state-of-the-art models including GPT-4, GPT-4 Turbo, Claude 3, Gemini 1.5, LLaMA 3, Mistral, Mixtral, and a wide range of open-weight transformer architectures. My work spans Retrieval-Augmented Generation (RAG), multi-agent systems, and custom fine-tuning across both cloud-native and hybrid enterprise environments. I am highly proficient in Python and Golang, and deeply experienced with modern AI development frameworks such as LangChain, LlamaIndex, Hugging Face Transformers, FastAPI, PyTorch Lightning, and NVIDIA NeMo. I've designed and deployed scalable ML and GenAI solutions using AWS (SageMaker, Bedrock, Lambda), GCP (Vertex AI, Gemini Al Studio), and Azure (OpenAI, Bot Framework, SynapseML), integrating function calling, tool-use, and multimodal inference across text, images, and audio. My hands-on expertise includes full-lifecycle ML operations using MLflow, Weights & Biases (wandb), BentoML, and KServe, alongside data engineering and analytics technologies such as Databricks, Apache Spark, Snowflake Cortex, Delta Lake, and streaming platforms like Kafka and Flink. I have deep experience with vector databases (Pinecone, Weaviate, Chroma, FAISS, Qdrant) and graph databases (Neo4j, TigerGraph) to implement RAG, GraphRAG, semantic search, knowledge graph-augmented retrieval, embeddings optimization, and multimodal retrieval fusion. I bring advanced knowledge of CI/CD for AI using Terraform, Pulumi, GitHub Actions, Jenkins, CircleCI, and Argo Workflows, and I'm well-versed in containerized and distributed AI deployment using Kubernetes, Helm, Kubeflow, Ray Serve, and service mesh tools like Istio. My background includes implementing secure, multi-tenant Al systems guided by SOC 2, NIST, CIS, and enforcing best practices through Kubernetes RBAC and API gateways such as Kong and Ambassador. I have extensive experience creating Al copilots, enterprise chatbots, and autonomous agent systems using CrewAI, LangGraph, AutoGen, and the OpenAI Assistants API. My work emphasizes high-quality reasoning through advanced prompt engineering, retrieval fusion techniques, context compression, toolchain design, and system prompt tuning. In addition, I am adept with observability and reliability frameworks including Prometheus, Grafana, OpenTelemetry, and DataDog, and have a strong foundation in A/B testing, RLHF, LLMOps, hallucination mitigation, and structured evaluation pipelines. I remain deeply engaged with emerging areas such as synthetic data generation, LLM caching, real-time streaming RAG, compiler-level acceleration (e.g., Mojo), vector search optimization, and edge Al inference using ONNX, TensorRT, and Jetson. Known for my ability to collaborate across teams, mentor engineers, and align AI initiatives with business objectives, I am committed to delivering responsible, explainable, and scalable AI solutions that connect cutting-edge research with real-world results.