PITTSBURGH, PA

Jim Bonant

Senior AI/ML Infrastructure Architect
& MLOps Engineer

13+ years building production-grade LLM platforms, RAG systems, and scalable hybrid AI infrastructure that actually ships.

CURRENTLY BUILDING AT NATURALLY ARTIFICIAL OF PENNSYLVANIA
ABOUT

I build the infrastructure
that makes AI reliable.

Senior AI/ML Infrastructure Architect and MLOps Engineer with 13+ years designing, deploying, and optimizing scalable hybrid cloud and on-prem AI platforms. I specialize in production LLM serving, Retrieval-Augmented Generation (RAG) systems, vector databases, and custom LLM solutions in Python, Rust, and C++.

I’ve led MLOps transformations that delivered 70–80% faster deployments, 99.99% uptime, and substantial cost and performance gains across enterprise environments.

Currently at Naturally Artificial of Pennsylvania, I architect bare-metal LLM inference platforms and containerized AI products that power real customer workloads at scale. Previously I led machine learning teams at PNC and drove major containerization and CI/CD initiatives at BNY Mellon.

13+
YEARS IN PRODUCTION AI & DEVOPS
99.99%
UPTIME ON CRITICAL PLATFORMS
DEPLOYMENT VELOCITY IMPROVEMENT
100k+
DAILY INFERENCES SUPPORTED
CAREER

Experience

March 2024 — Present
Naturally Artificial of Pennsylvania
Senior AI/ML SecOps Engineer

Architecting and deploying scalable hybrid bare-metal LLM inference platforms integrated with containerized applications and APIs. Supporting 100k+ daily inferences with sub-500ms latency (3× speedup).

  • Designed and led end-to-end MLOps + DevOps CI/CD pipelines (Jenkins, GitLab) for full-lifecycle model deployment and monitoring.
  • Built custom RAG systems including the S.A.R.A. executive AI agent and domain-specific variants achieving 95% retrieval accuracy.
  • Engineered complete infrastructure stack with comprehensive AI/ML SecOps (zero-trust, vulnerability scanning, encryption, AI-powered security gates).
July 2021 — March 2024
PNC
Machine Learning Team Lead

Led a cross-functional team of 4 engineers building and deploying production ML models and MLOps pipelines for financial analytics and risk applications.

  • Accelerated model iteration by 50% and improved accuracy through optimized data pipelines.
  • Delivered 60% faster time-to-production and 55% lower query latency for high-volume transaction systems.
  • Drove enterprise MLOps adoption (model versioning, automated testing, observability) and mentored engineers on container orchestration.
Feb 2018 — Aug 2022
Bank of New York Mellon
VP – Production Release Engineering

Led major modernization of release engineering and infrastructure in highly regulated environments.

  • Migrated multiple enterprise applications to Docker/Kubernetes, increasing deployment velocity 4× while maintaining 99.95% uptime.
  • Reduced release times by 60% and production incidents by 85% through GitLab CI/CD migration and Jenkins cleanup.
  • Implemented advanced git-flow strategies and Liquibase automation, cutting merge conflicts by 70%.
July 2012 — Feb 2018
Broadridge Financial
Senior DevOps Engineer

Principal build and automation engineer for enterprise financial reporting platforms.

  • Reduced manual deployment effort by 75% through Python, Shell, Groovy, and Jenkins/Bamboo automation pipelines.
  • Built custom Python deployment and QA tooling on Web2py and Django frameworks.
  • Led system provisioning with Chef and Ansible while supporting high-stability financial applications.
SELECTED WORK

What I’ve shipped

PRODUCTION AI AGENT

S.A.R.A. Executive AI Agent

Custom Retrieval-Augmented Generation system for executive decision support. Built full RAG architecture with specialized high-accuracy variants for marketing, form generation, and strategic analysis using vector databases and advanced prompting. Achieved 95% retrieval accuracy and 65% latency reduction.

Python • Rust • Vector DBs • LangChain • Secure Infrastructure
INFRASTRUCTURE

Bare-Metal LLM Inference Platform

Production hybrid platform combining bare-metal GPU inference with containerized front-ends and APIs. Supports 100k+ daily inferences at sub-500ms latency. Full MLOps lifecycle automation with comprehensive SecOps controls (zero-trust, encryption, AI-powered gates).

Kubernetes • Docker • Helm • Jenkins/GitLab CI • Prometheus/Grafana
EXPERTISE

Skills & Technologies

AI / ML INFRASTRUCTURE
LLM Inference & Serving RAG Architectures Vector Databases Model Deployment & Monitoring Edge AI Generative AI MLOps Pipelines
LANGUAGES & FRAMEWORKS
Python Rust C / C++ Go FastAPI LangChain SQL / Parquet
DEVOPS, CLOUD & SECURITY
Kubernetes / Docker / Helm Terraform Jenkins / GitLab CI Ansible / Chef AWS / GCP / Hybrid Cloud Prometheus / Grafana / ELK Zero-Trust Security AIOps & Observability
ACADEMICS

Education

University of Pittsburgh
2007 — 2009
Bachelor of Science in Computer Science
Community College of Allegheny County (CCAC)
2005 — 2007
Associate of Science in Software Development
LET’S CONNECT

Have a challenging AI infrastructure problem?

I’m always interested in speaking with teams building serious production AI systems or modernizing their MLOps practice.

I typically reply within 1–2 business days.