Independent Consultant in Agentic AI, Scientific Computing, and Quantitative Systems Architecture Contact: augusto [dot] kiel [at] gmail [dot] com ------------------------------------------------------------------------ Executive Profile Scientific Architect with a Physics background and elite tenure across Tier-1 financial institutions and scientific R&D organizations (Qontigo, SimCorp, Mercado Libre, AstraZeneca). I specialize in Research Software Engineering (RSE), High-Performance Computing, and Production-Grade AI Engineering. My work focuses on translating complex mathematical models into stable production systems, ranging from Neural Stochastic Differential Equations (NeuralSDEs) and Domain-Specific Languages (DSL) to Agentic Orchestration and Evaluation Frameworks (Evals). I help firms move research-quality models into production without losing mathematical correctness or computational performance. My Physics degree (Licenciatura, University of Buenos Aires) taught me to find the right level of abstraction before writing a line of code. Seven years across JP Morgan, Qontigo, SimCorp, and Mercado Libre taught me what production actually costs. Core expertise: High-Performance Computing • Neural Stochastic Differential Equations • Agentic AI platforms • Multi-asset pricing systems • Risk analytics • DSL development • Numerical optimization • Python/C#/Julia • System Architecture Engagement Focus: Performance Optimization Sprints, Agentic Platform Integration, Knowledge Engine Architecture, Agent Evaluation Frameworks, RSE Consulting, DSL Design, Architectural Design Sprints, Specialized Corporate Training. ------------------------------------------------------------------------ Consulting Services Agentic AI Engineering - Production-grade Model Context Protocol (MCP) server design and implementation - Multi-tenant enterprise authentication architecture (OAuth 2.0 PKCE, Grant Chains, CCG) - Competitive Intelligence Knowledge Engines: document ingestion pipelines, hybrid RAG, IR trace optimization - Automated Agent Evaluation (Evals) frameworks for task accuracy, tool-calling reliability, and retrieval stability - Multi-layer agent memory systems (episodic, semantic, working memory) and personalization engines - LLM security architecture and threat modeling for advanced reasoning models - Agentic cost engineering: pre-filtering, caching, and execution graph optimization Research Software Engineering (RSE) - Translating research-quality code into stable, reproducible production systems - Neural Stochastic Differential Equations (NeuralSDEs) and physics-informed ML for scientific simulation and quantitative finance - Standardizing analytical workflows from Jupyter notebooks to CI/CD-managed pipelines - Reproducibility, testing, and DevOps practices for R&D and Quantitative Finance teams - Scientific computing architecture for high-mathematical-complexity domains - Migration and modernization of legacy analytical systems High-Performance Computing (HPC) Sprints - Performance audit and optimization for numerical bottlenecks - Vectorization, caching, and parallelism strategies - 300% performance gains achieved in production systems - Real-time computational finance optimization - Multi-asset class pricing and valuation frameworks Domain-Specific Language (DSL) Architecture - Custom language design for complex pricing models - Interactive UI development for quantitative libraries (ipywidgets, Voilà) - Simplifying access to sophisticated mathematical models for non-engineer users Training & Knowledge Transfer - High-Performance Numerical Computing with Julia workshops - Research Software Engineering (RSE) best practices for scientific teams - Corporate workshops on quantitative finance and computational methods - Technical mentorship for quant and engineering teams - Interactive educational materials using Jupyter notebooks ------------------------------------------------------------------------ Education Licentiate degree in Physics, University of Buenos Aires (2011-2017) Thesis: Statistic Analysis and Numerical Modeling of Single Particle Trajectories: Diffusion and Confinement Mechanisms ------------------------------------------------------------------------ Signature Case Studies 1. The Performance Optimization Sprint (Qontigo) The Challenge: Critical risk calculations were too slow for real-time reporting due to unoptimized convertible bond pricing engines. The Solution: Led a forensic performance audit and implemented cache optimization strategies in the core C# numerical library. Outcome: Achieved a 300% performance gain, enabling real-time production risk reporting and significantly reducing Azure compute spend. 2. The Scientific AI Implementation (Research/SimCorp) The Challenge: Traditional Monte Carlo simulations for European Option Pricing were computationally expensive; standard AI lacked mathematical constraints. The Solution: Managed research into Neural Stochastic Differential Equations (NeuralSDEs) using Julia, combining deep learning with physical laws. Parallelly developed an LLM-based RAG system for querying complex financial documentation. Outcome: Demonstrated superior convergence speeds over traditional solvers and established a framework for “Safe AI” in financial contexts. 3. The “Quant Experience” Architecture (SimCorp) The Challenge: Quants struggled to interact with complex underlying pricing models, leading to errors and slow iteration. The Solution: Designed and developed a Proof-of-Concept Domain-Specific Language (DSL) and integrated interactive Jupyter-based UIs (Voila/ipywidgets). Outcome: Enabled non-engineers to safely construct and test pricing logic by abstracting underlying complexity. 4. The Enterprise Agentic Platform (AstraZeneca, Oncology R&D) The Challenge: An R&D agentic platform needed a complete external-integration surface, a measurable quality framework, and reliable knowledge retrieval — none of which existed. The Solution: Architected production-grade MCP servers with multi-tenant OAuth patterns; built automated Evals frameworks to quantifiably measure agent task accuracy and tool-calling reliability; designed document ingestion pipelines and optimized IR traces for a Competitive Intelligence Knowledge Engine; engineered multi-layer agent memory and personalization systems. Outcome: Delivered the full external integration layer to production; established reusable OAuth blueprints adopted for all subsequent integrations; reduced LLM token consumption by 13%+ through semantic pre-filtering and cache optimization; established an Evals suite tracking tool-calling accuracy and retrieval precision as ongoing KPIs. ------------------------------------------------------------------------ Professional Experience AstraZeneca Senior AI Engineer (Independent Contractor) March 2026 - Present | Remote Advancing the R&D Agentic Platform and strengthening internal developer enablement for agent-based workflows. - Enterprise Agentic Integrations: Architected and shipped the external integration surface of the R&D agentic platform, designing production-grade Model Context Protocol (MCP) servers with robust multi-tenant authentication patterns (OAuth 2.0 PKCE, Grant Chains, and Client Credentials). - Competitive Intelligence & Knowledge Engines: Built automated document ingestion pipelines to index enterprise sources into the LLM-Wiki/Knowledge Base. Tuned hybrid search (dense + sparse) and analyzed IR traces to cut retrieval latency and improve source hit rate. - Automated Agent Evaluations (Evals): Designed and ran automated evaluation frameworks measuring agent task-execution accuracy, tool-calling reliability, and retrieval stability across model updates. - Multi-Layer Memory & Personalization: Built user content personalization engines and structured agent memory systems—episodic, semantic, and working memory layers—so agents return context-relevant responses rather than generic ones. - LLM Security & Threat Modeling: Audited tool-use capabilities in advanced reasoning architectures, identifying and neutralizing critical tool-injection vectors and token-abuse vulnerabilities before execution in production. - Agentic Cost Engineering: Optimized execution graphs, achieving a 13%+ reduction in LLM token consumption through semantic pre-filtering and custom cache optimization layers. ------------------------------------------------------------------------ Phorma Co-founder & Research Software Engineer February 2026 - Present | Buenos Aires, Argentina Co-founded Phorma with Agustín Corbat to apply Research Software Engineering (RSE) to R&D and Quantitative Finance teams. Scientists keep their research focus; Phorma owns the engineering execution. - Transforming research-quality models and workflows into stable, reproducible production systems - Designing RSE architectures for scientific simulation and quantitative finance - Establishing engineering best practices (testing, CI/CD, reproducibility) in high-mathematical-complexity codebases Independent Consultant Quantitative Software & Scientific Computing January 2026 - Present | Buenos Aires, Argentina Providing specialized consulting services to financial institutions and technology companies: - Quantitative finance system architecture and development - Scientific computing and numerical software optimization - Technical leadership and team mentorship - Developer experience and tooling for analytical platforms Focus areas: Multi-asset pricing libraries, risk analytics systems, DSL development, performance optimization, Python/C#/Julia consulting. ------------------------------------------------------------------------ Mercado Libre Software Technical Lead, IT Staff / Financial Planning & Analytics June 2025 - March 2026 Leading technology strategy and managing 14 engineers across Financial Planning & Analytics for Latin America’s largest e-commerce ecosystem. Key Focus: Strategy, Standardization & AI Workflows - Architecting scalable financial planning and analytics platforms - Championing AI-assisted development workflows and clean architecture standards - Transforming ad-hoc Jupyter analyses into CI/CD-managed production systems Impact: 90% reduction in forecasting pipeline errors through RSE principles; 15% velocity increase across engineering team Technologies: Go, TypeScript, Python, BigQuery, Jupyter, CI/CD, distributed systems ------------------------------------------------------------------------ SimCorp Lead Software Engineer, Core Analytics March 2024 - May 2025 | 1 year 3 months Key Focus: Core Analytics & Quant UI - Developed Domain-Specific Language (DSL) POC for pricing model interaction - Created LLM-based RAG system for financial documentation Q&A - Integrated Quant UI with Axioma Risk UI for institutional investors - Redesigned libraries for Automatic Differentiation (AD) support Qontigo (Axioma Risk) Associate Principal, Core Analytics September 2020 - March 2024 | 3 years 7 months @akielbowicz-qontigo Key Focus: Core Quant Libraries & High-Performance Computing - Designed and developed core Quant Monorepo (C#) for Analytical Libraries - Managed NeuralSDE research internship for European Option Pricing using Julia - Built Axioma Pricing Library (APL) from ground up with 100% accuracy - Developed comprehensive curve construction library (rates, yields, discounts, spreads) - Led development of interactive UI (ipywidgets/voila) for Quant library access Impact: 300% performance gain on Convertible Bond Pricing Engine enabling real-time production calculations; “Exceptional Performance” rating (2023) J.P. Morgan Technology Analyst, Rates CIB July 2018 - August 2020 | 2 years 2 months - Built production-grade financial reporting systems with zero-downtime requirements for Rates CIB - Provided critical support to Rates Quant team, enhancing analytical capabilities - Delivered infrastructure for mission-critical reporting services ensuring compliance and reliability ------------------------------------------------------------------------ Open Source Contributor & Content Creator Scientific Software & Educational Tools February 2016 - Present | 9+ years @akielbowicz | YouTube: @SCA314 | GitHub: SCA314 - Creator of SCA314, an educational YouTube channel focused on software craftsmanship, scientific computing with Julia, and automated testing practices in Spanish - Development of interactive educational materials based on Jupyter notebooks - Contributions to scientific computing and data visualization projects - Created charly-vibes (site, GitHub) as a personal R&D initiative to explore the capabilities and limits of AI-assisted coding and agentic autonomous development across platforms - Educational content bridging academic knowledge and industry best practices ------------------------------------------------------------------------ Teaching Experience University of Buenos Aires - Professor of Calculus and Linear Algebra, CBC Engineering (December 2020 - July 2022) - Teaching Assistant on Summer Course of Optics and Thermodynamics for Biology and Geology (February 2015 - March 2015) - Science Communicator at Physics Department (March 2013 - December 2014) Southern International School - High School teacher of Physics, Mathematics and Information Technologies (2016) Publications - Shared Memory Semi-Implicit Solver for Hydrodynamical Instability Processes (2023) - Photon Counting Module based on Avalanche Photo-Diodes (2017) Speaking & Community Active participant in technology conferences and meetups as speaker and collaborator: - SciPy Latinoamérica 2022 (Argentina): Workshop presenter - Regular speaker at Python and Julia user groups in Buenos Aires - Represented Qontigo at ECI UBA (School of Information Sciences, University of Buenos Aires) All talks available at: talks.saxa.xyz ------------------------------------------------------------------------ Technical Skills Programming Languages Production: Python, C#, Julia Functional/Niche Languages: F#, Clojure Agentic AI: Model Context Protocol (MCP), LangChain, LangGraph, LlamaIndex, RAG pipelines, vector databases, AWS Bedrock, multi-agent systems, LLM Evals, enterprise OAuth (PKCE, OBO, CCG) Research Software Engineering: Reproducible workflows, scientific Python stack, numerical methods, automatic differentiation, stochastic differential equations, NeuralSDEs Quantitative Finance: Multi-asset pricing, risk analytics, derivatives valuation, curve construction, model calibration Architecture & Leadership: DSL design, API design, monorepo infrastructure, microservices, Technical Mentorship, Solution Design, Corporate Training Tools: Jupyter, Git, Docker, Azure, GitHub Actions, Visual Studio ------------------------------------------------------------------------ Available for consulting engagements globally (remote) and in Buenos Aires Contact: augusto [dot] kiel [at] gmail [dot] com ------------------------------------------------------------------------