Available to hire
Hi, I’m Clayton Smith, an AI Engineer and Data Scientist with 10+ years of experience architecting, scaling, and deploying ML, DL, NLP, CV, and GenAI solutions. I specialize in production-grade AI systems, leveraging state-of-the-art models and building robust, end-to-end AI platforms across cloud-native and hybrid environments.
I collaborate across teams to deliver responsible, explainable AI that aligns with business objectives, focusing on scalable architectures, reliable deployments, and measurable outcomes for customers and stakeholders.
Skills
Language
English
Fluent
Work Experience
AI / ML Engineer at Anlatan
October 1, 2023 - October 1, 2025Designed and automated end-to-end testing frameworks using Playwright, increasing test reliability and reducing manual QA workloads. Integrated test suites into CI/CD via GitHub Actions and Jenkins for automated regression validation across multi-environment deployments. Built cross-browser support (Chromium, Firefox, WebKit) with headless execution and trace tooling to accelerate releases. Architected scalable ETL/ELT pipelines with Apache Spark on Azure Databricks and optimized Delta Lake/Parquet performance. Trained specialized LLMs with spatial and textual data for geospatial reasoning. Delivered multi-agent AI workflows using LangChain, LangGraph, and CrewAI; implemented GraphRAG with Neo4j for contextual retrieval. Deployed secure, containerized AI platforms using Docker, Kubernetes, and Terraform; enforced multi-tenant access controls and security patterns (Kong/WAF). Built vector search stacks with Pinecone, FAISS, and Qdrant to power production RAG. Enabled real-time data proc
AI / ML Engineer / Generative AI Engineer at Deep AI
May 1, 2020 - September 1, 2023Directed end-to-end automation for a multi-role enterprise dashboard using Playwright; cut UI defects by 40% and enabled multi-tenant, role-based workflows. Built modular test fixtures and extensions; integrated CI-driven debugging with artifacts to improve feature visibility. Developed performant front-ends with vanilla JS, TypeScript, React, and Next.js; optimized rendering with debouncing, throttling, and lazy-loading. Engineered GIS-spatial data pipelines in Spark; created spatial knowledge graphs to support geospatial AI tasks. Implemented GraphRAG pipelines with FAISS/Neo4j for contextual retrieval. Led Palantir Foundry deployments in healthcare and logistics, including governance and row-level security. Implemented enterprise AI search with Glean across Confluence, SharePoint, and Slack. Built Azure AI chatbots and multimodal capabilities (QnA Maker, Form Recognizer, Face API). Delivered GPU-accelerated video analytics on Jetson via TensorRT; implemented 3D depth estimation. Ens
MLOps & Machine Learning Platform Engineer at Simple Practice
November 1, 2015 - April 1, 2020Designed and deployed AI-driven credit risk models, achieving a 25% reduction in default-prediction error. Automated legal document summarization workflows using transformer NLP. Built advanced customer segmentation models increasing marketing conversion by 30%. Implemented real-time fraud anomaly detection with 99% accuracy. Scaled production ML workloads with Kubernetes autoscaling, reducing cloud costs by 20%. Engineered PySpark-based data pipelines and Databricks ETL/ELT workflows; optimized feature engineering and data processing at scale. Integrated Azure AI Search with enterprise platforms and trained large-scale TensorFlow models on datasets exceeding 7TB. Containerized NLP models with Docker and deployed RESTful microservices via Flask/FastAPI. Modernized legacy systems into microservices; enforced HIPAA/GDPR/SOC 2 security; delivered real-time and batch inference using AWS SageMaker Endpoints. Built OCR pipelines (YOLOv7, CRNN, MobileBERT) achieving 95% accuracy. Developed mu
AI/ML Engineer at Anlatan
October 1, 2023 - October 1, 2025Designed and deployed end-to-end AI/ML capabilities for production systems, including multi-agent LLM workflows with LangChain and CrewAI, implemented GraphRAG with Neo4j for contextual retrieval, and built real-time fraud detection pipelines on SageMaker and Lambda. Implemented memory buffering, vector stores, and prompt compression for low-latency inference, and established observability with Prometheus, Grafana, and OpenTelemetry. Led enterprise AI search and knowledge-graph initiatives, and integrated secure, multi-tenant patterns with RBAC and API gateways.
AI/ML Engineer / Generative AI Engineer at Deep AI
May 1, 2020 - September 1, 2023Directed end-to-end automation for a multi-role enterprise dashboard; built modular Playwright test suites for multi-tenant environments; engineered scalable data pipelines for GIS and geospatial reasoning; fine-tuned GPT/Claude/Gemini models for legal and financial analytics; deployed LLM-powered chatbots and copilots; established end-to-end MLOps with MLflow, Kubeflow, and CI/CD automation; implemented RAG-based retrieval and graph-based knowledge graphs.
AI / ML Engineer / Software & Data Engineer at Anlatan
October 1, 2023 - October 1, 2025Designed and deployed production-grade GenAI and ML systems; automated end-to-end testing with Playwright; integrated CI/CD pipelines with GitHub Actions and Jenkins; implemented cross-browser testing; built scalable ML/data pipelines on Azure Databricks; implemented RAG, multi-agent reasoning with LangChain; built vector search stacks with Pinecone/FAISS; deployed secure, multi-tenant AI platforms; developed real-time fraud detection pipelines; tuned GPT, Claude, Gemini models for domain tasks; built CLV/RFM models; established observability with Prometheus & OpenTelemetry; delivered GenAI demos and POCs.
AI / ML Engineer / Data Engineer at Deep AI
May 1, 2020 - September 1, 2023Directed end-to-end automation and ML platform initiatives; built modular Playwright-based UI tests; established CI/CD-driven debugging; developed robust data pipelines for GIS/spatial data using Spark; built vector search and knowledge-graph applications; delivered scalable ML services on SageMaker/Lambda; implemented end-to-end ML lifecycle tooling (MLflow, wandb) and governance; delivered production-grade GenAI integrations and NLP pipelines.
Education
Master of Science in Computer Science at The University of Texas at Dallas
January 11, 2030 - January 1, 2015Bachelor of Science in Mathematics and Computer Science at University of North Texas
January 11, 2030 - January 1, 2013Master's in Computer Science at The University of Texas at Dallas
January 11, 2030 - January 1, 2015Bachelor's in Mathematics and Computer Science at University of North Texas
January 11, 2030 - January 1, 2013Master’s in Computer Science at The University of Texas at Dallas
January 11, 2030 - January 1, 2015Bachelor’s in Mathematics and Computer Science at University of North Texas
January 11, 2030 - January 1, 2013Qualifications
Industry Experience
Software & Internet, Professional Services, Education, Healthcare, Media & Entertainment, Other
Skills
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