🚀 Engineering Intelligence at Scale: Where Mathematical Rigor Meets Industrial Impact
🔬 Full-stack AI/ML, DevOps, and Systems Engineering | Nuclear-Powered AI Research | Ethical & Sustainable Innovation

I design and deploy enterprise-grade machine learning ecosystems that transcend model development—integrating algorithmic robustness, system-level resilience, ethical governance, and industrial-scale efficiency. My work bridges theoretical AI research with mission-critical production systems, enabling organizations to derive measurable ROI from intelligent automation, predictive foresight, and autonomous decision-making.

This is not merely ML engineering—it is AI systems architecture as a core competitive lever.

• Built end-to-end CI/CD workflows using Terraform, Kubernetes, ArgoCD, Prometheus/Grafana — achieving 99.98% uptime across 20+ microservices.
• Implemented model versioning (DVC), lineage tracking (Metaflow), drift detection (Evidently AI) for auditability and compliance (EU AI Act, NIST AI RMF).
• Automated hyperparameter tuning via Ray Tune + Bayesian Optimization, reducing training cycles by 43% while maintaining ≥99.7% F1-score stability.

💡 System-Level Insight: ML pipelines are cyber-physical systems subject to feedback loops, data decay, and adversarial perturbations. We treat them as such.

• Engineered real-time inference engines processing 10M+ events/hour using TensorFlow Serving + gRPC + ONNX Runtime, achieving sub-15ms latency on edge nodes.
• Deployed multi-modal fusion architectures (vision + tabular + time-series) for industrial anomaly detection, improving fault prediction accuracy by 68% vs. rule-based systems.
• Pioneered causal discovery frameworks (PC-algorithm, DoWhy) to de-risk business decisions—moving beyond correlation to actionable causality.

🔢 Mathematical Foundation: Leveraging information geometry, differential privacy, and stochastic calculus to quantify uncertainty propagation through deep networks.

• Developed certified XAI modules compliant with EU AI Act Article 14:
  • SHAP/LIME interpretability layers
  • Counterfactual explanation engines (DiCE)
  • Model cards and impact assessments (Model Cards for Model Reporting)
• Built bias mitigation pipelines using adversarial debiasing and reweighting techniques (IPW), reducing demographic disparity by up to 74% in credit scoring models.

✅ Meta-Strategy: Ethics is not an afterthought—it is a first-order constraint in system design.

• Led feasibility studies on micro-nuclear reactor integration into AI compute clusters.
• Modeled energy density gains: ~300x over lithium-ion batteries.
• Quantified carbon footprint reduction: ~120,000 tons CO₂/year per 10 MW facility.
• Proposed hybrid fusion-byproduct energy harvesting systems for long-duration LLM training runs.

🔬 Engineering Implication: Requires synergy between plasma physics, thermal-hydraulics, power electronics, and distributed computing.

• Prototyped ZK-SNARKs for model sharing in federated learning environments — enabling verifiable computation without exposing raw data.
• Integrated post-quantum cryptography (PQC) standards (CRYSTALS-Kyber, Dilithium) into model delivery chains.

• Directed R&D in RL for supply chain optimization, resulting in 19% lower inventory costs and 27% faster delivery times.
• Published white papers on LLM fine-tuning via LoRA + MoE, reducing hallucination rates by 52%.
• Advised CTOs and board executives on AI maturity assessment frameworks, aligning tech roadmaps with financial KPIs (LTV/CAC, ROIC).

📊 Strategic Leverage: I don’t just build models—I architect value streams where AI becomes a profit center.

Currently exploring:

• Autonomous AI Agents with self-repairing logic and meta-learning (based on Neural Turing Machines, differentiable programming).
• Self-optimizing neural networks using evolutionary algorithms (NEAT, CMA-ES) for dynamic adaptation.
• Energy-aware model compression: Applying thermodynamic bounds on information processing to guide pruning and quantization.
• Quantum Machine Learning (QML): Hybrid variational circuits for combinatorial optimization (e.g., portfolio allocation, circuit layout).

"The next generation of AI won’t be about bigger models—it will be about smarter systems that operate within physical, economic, and ethical constraints."

I partner with organizations that seek more than incremental improvements. I work with:

• Fortune 500 enterprises modernizing legacy analytics stacks into real-time intelligence platforms.
• High-growth startups scaling from MVP to globally distributed AI services with built-in compliance and security.
• Government and defense agencies developing resilient, explainable AI systems under strict regulatory scrutiny.

Contact: optimoter@gmail.com
LinkedIn: linkedin.com/in/jorge-terceros-273155168
GitHub: github.com/LemmaUX
Portfolio: [Coming Soon – Enterprise AI Case Studies & White Papers]

🔐 Technology with mathematical fidelity. Innovation with systemic foresight.
💼 Let’s build what’s next—not just possible, but necessary.

© Dr.T | 2025 | All rights reserved. Intellectual property protected under open science principles with commercial application licensing available.