Software architect and systems engineer with more than 12 years of experience designing and maintaining high-scale platforms, distributed systems and long-lived software environments.

I focus on clarity, predictable behavior, and architectural stability over time. My work emphasizes maintainability, correct data modeling and the reduction of operational complexity.

• Led technical strategy and implementation for platforms serving audiences in the order of millions.
• Architected distributed pipelines for media processing, metadata extraction, analytics layers and automated reporting.
• Built internal frameworks and front-end architectures to enforce consistency and scalability across multiple products.
• Developed analytical, attribution and performance optimization systems for large-scale marketing and behavioral data environments.
• Designed and maintained execution environments for algorithmic research and backtesting workflows.
• Implemented observability layers, structured logging, error taxonomies and reliability programs for long-running services.

I specialize in stabilizing, scaling and extending complex systems without compromising clarity.

• Mathematical modeling for systems behavior, complexity boundaries and scaling curves.
• Algorithmic problem framing and performance tradeoff evaluation.
• Data and schema normalization for long-term model integrity.
• Statistical analysis for multi-source attribution and trend isolation.
• Applied information theory and entropy minimization in distributed systems.
• Functional programming patterns for deterministic and testable execution flows.
• Mental models for system limits: latency budgets, throughput ceilings and memory discipline.

Languages

• TypeScript, JavaScript
• Python
• Go
• Rust
• C++ / C
• C#

Front-End

• React, Next.js
• Svelte, SvelteKit
• Astro
• Remix
• Vue, Nuxt.js
• Tailwind and internal design systems

Backend & Distributed Systems

• Service Architectures:
  • Node.js (high-throughput service design, modular monoliths with enforced boundaries, service virtualization)
  • Microservices and polyservice architectures with shared contracts, versioned protocol evolution, anti-corruption layering
• Event-Driven & Queueing Systems:
  • Distributed task orchestration with BullMQ, Redis Streams, RabbitMQ and custom scheduling pipelines
  • Deduplication, idempotency signatures, replay-based recovery, poison-queue isolation
  • Multi-queue architectures for priority segmentation, throttled pipelines and dynamic backpressure control
• APIs & Interfaces:
  • REST, GraphQL (federated schema design), gRPC with streaming payloads and typed contracts
  • Edge-hosted request evaluation, zero-copy payload paths, envelope compression and chunked transport
• Concurrency & Execution Models:
  • Actor-based coordination, async job channels, concurrent I/O routing, multi-node consistency guarantees
  • Isolation and sandbox execution for untrusted code (VM, WASM, seccomp strategies)
• Distributed Coordination & Cluster Patterns:
  • Gossip protocols, cluster membership convergence, leader election, quorum enforcement
  • Consistency models (strong, causal, eventual) chosen per subsystem based on latency budgets
• Pipeline Processing & Streaming Data:
  • Real-time event streams, multi-stage functional pipelines, dynamic DAG execution
  • Stateful and stateless stream aggregators, watermarking and late-event compensation
• Internationalization & Dynamic Sequence Engines:
  • Runtime composition of UI/UX content in multiple languages via layered lookup models
  • Distributed translation caches, fallback trees, context-scoped language rendering in complex workflows
• Deployment & Lifecycle:
  • Automated multi-environment rollout, blue/green, canary, dark launching
  • Cluster bootstrap automation, ephemeral node pools, rolling convergence without downtime

Data & Storage

• Relational: PostgreSQL, MySQL/MariaDB (query planning, indexing strategies, partitioning, normalization for long-lived models).
• Columnar & Analytical: ClickHouse (large-scale analytical queries, materialized views, cluster sharding), DuckDB for local analytical workflows.
• Search & Retrieval: Elasticsearch / OpenSearch (relevance tuning, analyzers, BM25 weighting), Meilisearch / Typesense for low-latency search.
• Caching Layers:
  • Redis (LRU/LFU caching, pub/sub, streams, consumer groups)
  • Redis Cluster / Sentinel failover
  • KeyDB multi-threaded caching models
  • CDN edge caching strategies (cold/warm cache paths, pre-warming, invalidation heuristics)
• Time-Series, Event, and Logging Storage:
  • InfluxDB, TimescaleDB
  • Kafka log stores for distributed event replay
• Object Storage & Media Delivery:
  • S3-compatible storage (multi-region replication, lifecycle policies)
  • Cloudflare R2 & Workers KV / Durable Objects / D1
• Data Modeling Strategy:
  • Schemas designed to survive version drift and incremental product evolution
  • Stable record identity, reversible transformations, referential clarity over time

Media & Streaming

• End-to-end transcoding and packaging workflows for large media catalogs across anime, OTT streaming and adult entertainment platforms (large-scale webcam / live-stream networks).
• FFmpeg-based pipeline engineering: multi-step processing graphs, waveform extraction, silence detection, scene-based segmentation, hardware-accelerated transcodes (NVENC/VAAPI).
• HLS/DASH adaptive bitrate ladder generation with constrained bitrate and perceptual quality optimization.
• Implementation of subtitle, lyric, chaptering and metadata enrichment layers (ASS, WebVTT, SRT, forced narratives, dual-language overlays).
• CDN-aware origin setups and delivery paths with segment-level caching strategies.
• Quality analysis and compliance workflows (PSNR, VMAF, SSIM) for stream optimization and reference-based comparison.

AI / ML / Algorithmic Systems

• Embedding-based similarity search and semantic retrieval layers
• Model chaining, summarization flows and retrieval-augmented generation (RAG)
• Vector indexing, approximate nearest neighbor search, dimensionality considerations
• Feature engineering for behavioral data and automated segmentation

Infrastructure

• Distributed deployments based on Docker, container lifecycle optimization and layered build strategies.
• Reverse proxying and routing control with NGINX and Traefik (rate shaping, origin shielding, service segmentation).
• CDN & Edge Compute:
  • Cloudflare Pages, Workers, KV, Durable Objects, R2, Queues
  • Edge routing, rewrite strategies, caching rules, tokenized image & media delivery
  • Firewall, bot filtering, header policy, DDoS tiering & shielding
• CI/CD orchestration across multi-environment pipelines (blue/green, canary, staged rollout systems).
• Identity-scoped cloud automation using service accounts, granular IAM policies and secure credential isolation.
• Multi-layer observability:
  • Structured logging, trace pipelines, metrics and automated degredation signaling.
  • Prometheus, Grafana, Loki, Tempo, ELK/OpenSearch stacks.
• Reliability Engineering:
  • Error budgeting, failure envelope analysis, progressive incident elimination.
  • Latency budgets, throughput ceilings, memory discipline and capacity planning.

I have managed large-scale, high-budget advertising ecosystems (multi-million USD annual spend) where operational precision and signal integrity directly influence revenue. My approach focuses on automation, verifiable attribution, adaptive optimization and minimizing performance entropy across pipelines.

Paid Media Strategy & Optimization

• Campaign planning and execution across: Google Ads, Meta Ads, TikTok Ads, Microsoft Ads, Taboola, Outbrain, private DSPs
• Advanced optimization of CPA, CPM, CPC, ROAS and LTV using:
  • Conversion window analysis and bid adjustment heuristics
  • Real-time signal scoring and dynamic budget reallocation
  • Behavioral segmentation, cohort-based targeting and safe scaling strategies

Automated Campaign & Creative Systems

• Automated generation of ad variations (text, layouts, targeting configurations)
• Continuous experimentation engines using bandit algorithms (Thompson Sampling, UCB1)
• Creative iteration pipelines with AI-assisted copy, visual adaptation and multilingual rollout
• Programmatic execution of campaign life cycles (creation → testing → scaling → retirement)

Attribution & Measurement

• Deterministic and probabilistic attribution frameworks: UTM + contextual fingerprinting + MTA blends
• Integration with Google Analytics Data API, BigQuery, Microsoft Clarity, Appsflyer and SKAdNetwork
• Resolution of cross-device and cross-region discrepancies
• Signal clarity enforcement through noise modeling, fraud suppression and invalid traffic isolation

Compliance, Policy & Rights

• Rights and licensing management, distribution agreements and DMCA/DRM constraints
• GDPR, CCPA, LGPD and data minimization compliance
• Implementation and governance of CMPs supporting IAB TCF v2.2
• Policy-safe creative and audience structuring across regulated verticals

Data Engineering for Growth

• Audience clustering using RFM, behavioral windows, semantic similarity and lifetime value projections
• Predictive scoring for churn, conversion likelihood and spend elasticity
• Feature store design for real-time optimization models
• Operational and analytical dashboards using ClickHouse, BigQuery, Redshift, Grafana and Metabase

Operational Excellence

• Budget entropy monitoring for waste reduction and anomaly detection
• Intelligent alerting for creative fatigue, segment drift and funnel instability
• Campaign-level decision trees and attribution path audits

My current work revolves around designing, operating and evolving platforms that must remain stable, correct and observable under continuous growth, high concurrency and constantly shifting business requirements.

I work with:

• High-reliability distributed computation environments, where execution determinism, memory behavior and failure boundaries must be explicitly understood.
• Multi-region content and traffic distribution, serving clusters of platforms reaching millions of daily users with median latencies as low as 40–60ms.
• Advanced attribute and schema modeling, for systems intended to persist for years without requiring migrations that break data lineage or operational continuity.
• Formal reasoning about correctness, ensuring that behaviors are measurable, predictable and reversible — particularly in pipelines with irreversible transformations.
• Performance refinement across compute, I/O and render paths, minimizing latency and resource waste without sacrificing readability or architectural clarity.
• Semantic representation models, chained reasoning pipelines and retrieval systems, integrating structured embeddings, embeddings-based joins, symbolic layers and long-context model orchestration.
• Systemic simplification, reducing entropy and long-term operational drag, ensuring systems can grow without growing more complicated.

Many of the systems I build are not short-lived products — they are long-run infrastructure foundations that must remain comprehensible and adaptable years later, regardless of scale.

Everything I build has a single priority: to make my clients’ systems last, scale and remain understandable — even as complexity, traffic and operational demands increase.

I design architectures that:

• Maintain clarity over time, regardless of team size or turnover.
• Scale without introducing architectural drift or silent brittleness.
• Fail in controlled and predictable ways, with explicit recovery paths.
• Optimize for long-term stability, rather than short-lived wins or hype.

I architect systems with an emphasis on:

• Data integrity and lineage.
• Deep observability and traceability.
• Realistic operational cost and maintainability.
• Consistency of execution at every layer.

I prioritize systems that endure, because endurance is the hardest thing to build.

At present, I am not accepting new external work — my ongoing commitments already involve:

• Maintaining large-scale distributed media and data pipelines.
• Operating multi-language, multi-region traffic distribution networks.
• Supporting platforms that collectively reach billions of monthly requests.
• Ensuring strict performance guarantees, with latencies frequently under 50ms at global scale.

My work continues — quietly, consistently — at large scale.