Principal, Agentic Engineering Systems · "I ship with agents."
PhD Mathematics · 15+ years production AI/ML.
I build self-improving engineering systems that ship PRs against real enterprise codebases, including personal agents that learn each engineer's context and expertise, and the harness that keeps them in bounds.
Most recently I led an AI tooling team at a Big 4 professional services firm, building production LLM tooling on open-source agent/RAG components. Before that I designed autonomous engineering systems for real enterprise software workflows. They used the same channels any engineer does: repos, PRs, CI, and Slack.
That work, including unsupervised runs on real production code, made the next bottleneck obvious: capability isn't the hard limit anymore. Authority, trust, and blast radius are.
I've hardened the design into an Agent Harness architecture: authority boundaries, blast-radius scoring, multi-agent consensus, and human approval routing. Other teams reached out to learn the approach.
Background: led MLOps adoption across 30+ business units; drove $48M in annual data-cost reduction; built production RAG and Slackbot systems for public sector and Fortune-scale telecom. ML roots in signal processing, frame theory, NLP, time-series, and applied math. US patent pending.
Looking for work building governed engineering-agent fleets: personal agents that learn each engineer's context and expertise, shared team agents that coordinate across repos and workflows, and the control plane that keeps it safe enough to run in prod. Also a good fit for agentic SDLC platforms, internal AI infrastructure, or principal-architect work. Prefer C2C/1099, open to full-time or fractional.
Role: Staff/Principal ML Engineer (1099/C2C; full- or part-time)
15+ years applied ML—from Los Alamos Labs to Fortune 500 production. PhD in Mathematics. $48M cost savings via MLOps unification; 30x faster RAG serving millions; unified ML platform adopted by 34 business units.
Specialty: Moving models from notebook to revenue: LLMs/RAG; MLOps (K8s/GitOps); high-scale serving.
Seeking: Staff-level IC role where ML/AI is core. I unblock the toughest issues and level up teams.
Email: paperaxis.refining795@simplelogin.com (email for full resume and references)
I'm a seasoned full-stack AI/ML/DS technical expert with 15+ years of experience driving innovation and delivering high-impact solutions across startups, mid-size companies, and large-scale organizations. My unique blend of deep technical expertise, academic background, and leadership experience positions me as an ideal candidate for roles requiring both hands-on technical skills and strategic vision.
Key Highlights:
• ML software patent pending, demonstrating innovative thinking in the field
• Extensive experience in Generative AI, LLM training and inference, and RAG systems
• Expert in MLOps, DevOps, and full-stack production engineering
• Strong leadership background, including mentoring teams and driving organizational change
Technical Proficiencies:
• AI/ML: Deep learning, NLP, scale ML training and inference
• MLOps: Kubernetes, GitOps, CI/CD pipelines, scalable ML platforms
• Data Science: Statistical analysis, modeling, data contracts and pipelines
I'm passionate about leveraging cutting-edge AI/ML technologies to solve complex business problems and drive innovation. I excel in roles that combine technical depth with strategic thinking, whether as a hands-on technical leader or a visionary individual contributor.
Looking for: High-impact projects and roles in AI/ML leadership, technical architecture, or senior individual contributor positions. Open to full-time, contract, or consulting engagements. Please email me for a detailed resume.
Remote: Yes
Willing to relocate: No
Technologies: Agentic engineering systems, Agent Harnesses, AgentOps, multi-agent orchestration, self-improvement loops, LLMs, RAG, evals, Python, FastAPI, MLOps/LLMOps, Kubernetes/GitOps, AWS/GCP/Azure, Terraform, observability, vector databases
Resume: Available upon request
Email: paperaxis.refining795@simplelogin.com
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Principal, Agentic Engineering Systems · "I ship with agents."
PhD Mathematics · 15+ years production AI/ML.
I build self-improving engineering systems that ship PRs against real enterprise codebases, including personal agents that learn each engineer's context and expertise, and the harness that keeps them in bounds.
Most recently I led an AI tooling team at a Big 4 professional services firm, building production LLM tooling on open-source agent/RAG components. Before that I designed autonomous engineering systems for real enterprise software workflows. They used the same channels any engineer does: repos, PRs, CI, and Slack.
That work, including unsupervised runs on real production code, made the next bottleneck obvious: capability isn't the hard limit anymore. Authority, trust, and blast radius are.
I've hardened the design into an Agent Harness architecture: authority boundaries, blast-radius scoring, multi-agent consensus, and human approval routing. Other teams reached out to learn the approach.
Background: led MLOps adoption across 30+ business units; drove $48M in annual data-cost reduction; built production RAG and Slackbot systems for public sector and Fortune-scale telecom. ML roots in signal processing, frame theory, NLP, time-series, and applied math. US patent pending.
Looking for work building governed engineering-agent fleets: personal agents that learn each engineer's context and expertise, shared team agents that coordinate across repos and workflows, and the control plane that keeps it safe enough to run in prod. Also a good fit for agentic SDLC platforms, internal AI infrastructure, or principal-architect work. Prefer C2C/1099, open to full-time or fractional.
Email for full resume and references.
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