Senior AI/ML Engineer Applied AI & Data Science

BRIAN KING

Senior AI/ML Engineer Applied AI & Data Science

512-***-**** *********@****.*** Austin, TX Remote linkedin.com/in/brianking387 SUMMARY

Senior AI/ML Engineer with a background in statistical modeling, ML research, agentic systems, and developer tooling. Built and shipped multi-agent LLM systems, RAG pipelines, and MCP-based tooling and ML pipeline optimization. Delivered measurable improvements including 60% faster developer onboarding, an 85% reduction in LLM hallucinations, and over 80% root-cause identification accuracy.

TECHNICAL SKILLS

AI / LLM: Generative AI · Agentic AI · LLMs · AI Agents · RAG · MCP · Tool Calling · Prompt Engineering · LLM Evaluation · LangGraph · LangChain · LlamaIndex

Machine Learning: Statistical Modeling · Predictive Modeling

· NLP · Model Evaluation · PyTorch · TensorFlow · Scikit-learn

· XGBoost · Transformers

Programming / Data: Python · SQL · R · Pandas · PostgreSQL

· Vector Databases · Milvus · Qdrant · pgvector

MLOps / Cloud: Model Deployment · Model APIs · Model Monitoring · Docker · AWS · Azure · Databricks

Software Engineering: FastAPI · REST APIs · Microservices · TypeScript · Next.js · Git

PROFESSIONAL EXPERIENCE

Senior AI Engineer Arm May 2026 – Present

Building agentic AI tooling, evaluation infrastructure, and developer workflows to help software developers migrate to Arm and optimize workloads on Arm platforms.

• Architected an Arm MCP Server in Python using FastMCP, enabling AI agents inside IDEs like GitHub Copilot, Claude Code, and Gemini CLI to discover and chain cloud migration workflows, reducing developer onboarding time by 60%.

• Engineered a containerized RAG knowledge system using Milvus and LlamaIndex for document orchestration, with LangSmith-based execution tracing and automated Python pipelines for zero-downtime vector embedding updates; reduced LLM hallucinations by 85% while lowering inference costs.

• Evolved a conversational chatbot into an agentic engineering assistant that automated design-critical SoC calculations

(power, latency, thermal, routing) and technical knowledge retrieval for AGI CPU team.

• Architected a custom agentic harness (Codex-5.6 Luna) with MSAL-integrated tools for secure access to internal engineering resources.

• Orchestrated multi-agent workflows across GPT-4 and Claude using LangGraph and LangChain, implementing dynamic model routing and fallback strategies to optimize token usage, cost, and system reliability. Senior AI Engineer / Senior ML Research Engineer Arm Jul 2023 – May 2026 Built applied ML, data science, LLM, and AI platform systems that improved internal engineering workflows across hardware verification, performance analysis, root-cause investigation, and company-wide AI adoption.

• Developed and fine-tuned a multi-agent LLM system to identify root causes of CPU benchmark performance degradation, achieving >80% root-cause identification accuracy on a held-out evaluation set.

• Productionized the system as a microservice pipeline with Python, AWS SQS, and ECS, and integrated it into CLI tooling used by performance engineers.

• Built and launched a company-wide AI platform for sharing prompts, MCPs, AI solutions, and workflows, later surpassing 4,500 users across Arm.

• Productionized feature selection for a large-scale ML pipeline, reducing preprocessing time by 30%, training time by 66%, and peak memory by 75%.

Data Scientist Cervello Jun 2020 – Jul 2023

• Developed and deployed machine learning models using scikit-learn to pinpoint accounts buying below expected

‘capacity’ using KNN regression weighted by XGBoost feature importance; first iteration uncovered major targets and model was scaled nationwide to identify hundreds of businesses for further scrutiny.

• Developed a Python NLP pipeline for scraped e-commerce data combining string matching based on bespoke dictionaries with keyphrase classification model using TF-IDF embedding; model output was linked to sales data in Tableau dashboard applications to provide insight into market trends.

• Developed and trained time-series forecasting models in Azure Databricks to generate category-level spirit sales forecasts, enabling more informed commercial planning, demand forecasting, and data-driven decision-making for industry leaders.

• Developed interactive P&L reporting dashboards and an underlying data model that consolidated multiple data sources using Power BI for a major telecommunications company. This solution provided real-time financial insights that allowed executives to make data-driven decisions.

• Built project demos to evaluate capabilities of ML environments, e.g. Alteryx and Sagemaker Studio. Graduate Teaching Assistant Rice University Aug 2018 – May 2020

• Developed solutions, graded assignments, and supported instruction for graduate-level Mathematical Statistics, including measure-theoretic probability and statistical inference.

• Designed review sessions and created solutions for introductory course (Statistics for Data Science) during 3 semesters.

• Compiled custom datasets from aerial images and Census data for students to explore in Multivariate Statistics course project (STAT541), supporting data cleaning, exploratory analysis, visualization, and statistical modeling. Education Data Analyst Rice360 Institute for Global Health Technologies Oct 2018 – May 2019

• Wrangled, processed, and cleaned data for statistical analysis of educational ‘interventions’ in student subgroups using R, Excel, and SQL. Visualized the data and deployed the dashboard by RShiny and Plotly.

• Employed causal inference methods (doubly robust regression with matching on key confounders) to estimate treatment effect; results and interpretations were included in research article describing success of a course-based undergraduate research experience in the biosciences at Rice University. EDUCATION

Bachelor of Science Mathematics and Statistics Baylor University 2014 – 2018

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