Dalton Payne

contact@daltonpayne.ai | linkedin.com/in/daltonpayne | daltonpayne.ai

Summary

AI engineer building production agent systems for regulated industrial domains — nearly two years shipping AI agents across Drilling, Completions, and Safety at Devon Energy, then building the agent layer of an industrial AI platform at Collide.

Experience

Collide — Member of Technical Staff (Remote)

January 2026 – August 2026

  • Shipped the platform's subagent pattern — declarative templates discovered via Python entry points with a transactional instantiation service — and built primitive subagents on it over tools authored by domain experts.
  • Owned an end-to-end domain workflow integration: ported a standalone public-regulatory-data app into the platform across five services with a coordinated schema migration and agent-tool registration. Set the pattern for subsequent workflows.
  • Extended document ingestion to industrial formats (LAS well logs, Outlook .msg, .pptx) and replaced direct cloud-storage URLs with an authenticated streaming proxy across three services, closing a token-leak risk.
  • Built and maintained curated evaluation datasets across multiple production agents using LangSmith; ran iteration cycles surfacing drift between model and prompt changes.

Devon Energy — Oklahoma City, OK

June 2022 – January 2026 (3 yrs 8 mos)

AI Engineer (June 2024 – January 2026)

  • Lead engineer for production AI agents serving Drilling, Completions, and Safety in a regulated, high-consequence environment — RAG knowledge-base plugin + tool-calling against Snowflake, with versioned prompts and curated eval datasets.
  • Presented "Leveraging AI Agents to Navigate Safety Data" at the 2025 UTA Oil & Gas Conference.
  • Designed a multi-agent NL-to-SQL workflow — 6 task-specialized agents with task-tuned temperatures, ported from n8n to Python (pydantic-ai) for reuse.
  • Built a production ETL + analytics dashboard for foreman field audits on Snowflake, Databricks, Azure OpenAI (gpt-4o), and Dash Enterprise — incremental processing with threshold gating and LLM caching.
  • Authored a shared agent template + prompt-generator adopted across D&C agents; mentored an intern and taught a recurring "Teacher Thursday" session for engineers and leadership.

Data Science Intern (August 2022 – May 2024)

  • Built data pipelines and ML models on Databricks against Snowflake and OSIsoft PI, including a predictive maintenance model for an artificial lift system, scoped with petroleum and operations SMEs.

Selected Projects

kalashnikov.ai — Founder & Sole Engineer (2026 – Present)

  • AI reference platform with an autonomous extraction pipeline producing passage-level citations; full ownership on Cloudflare D1, Workers, and R2.

Technical Skills

  • Agent engineering: LangChain ecosystem, tool-calling, RAG, subagent patterns, eval datasets and iteration loops, MCP, prompt versioning
  • Data systems: Snowflake, Databricks, Alation, OSIsoft PI
  • Languages & frameworks: Python (FastAPI, pydantic-ai, pandas, scikit-learn), SQL, TypeScript
  • Cloud / Infra: Azure (OpenAI, Functions, APIM), Cloudflare, Docker
  • Certifications: FAA Part 107 Commercial Drone Pilot (2024)

Education

B.S. Data Science, University of Central Oklahoma — May 2024 (GPA 3.68)

Awards

  • Leonard-Murray Statistics Endowed Award (2023) and John Taylor Beresford Endowed Scholarship for Computer Science (2022) — UCO's largest STEM scholarship awards.