Acting as a Fractional CTO and Lead Engineer, delivering production-grade solutions in Quantitative Finance and Multimodal Generative AI pipelines.

🚀 𝗤𝗨𝗔𝗡𝗧𝗜𝗧𝗔𝗧𝗜𝗩𝗘 𝗙𝗜𝗡𝗔𝗡𝗖𝗘 𝗦𝗬𝗦𝗧𝗘𝗠 (𝗣𝘆𝘁𝗵𝗼𝗻/𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴)

• Engineered a standalone 𝗖𝗼𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗘𝗻𝗴𝗶𝗻𝗲 for pairs trading strategies, focusing on robustness and production deployment rather than ad-hoc scripts.

• Implemented rigorous statistical tests (ADF, CADF) within a modular Python codebase for high-performance backtesting and signal generation.

• Tech Stack: Python, NumPy, Statsmodels, Time-Series Analysis.

🚀 𝗦𝗘𝗟𝗙-𝗛𝗢𝗦𝗧𝗘𝗗 𝗚𝗘𝗡-𝗔𝗜 𝗜𝗡𝗙𝗥𝗔𝗦𝗧𝗥𝗨𝗖𝗧𝗨𝗥𝗘 & 𝗔𝗨𝗧𝗢𝗠𝗔𝗧𝗜𝗢𝗡

• Architected a 𝗰𝗼𝘀𝘁-𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁, 𝗽𝗿𝗶𝘃𝗮𝘁𝗲 𝗰𝗹𝗼𝘂𝗱 𝗔𝗜 𝗲𝗻𝗴𝗶𝗻𝗲 for a content production studio. Migrated workflows from expensive public APIs to self-hosted, optimized open-source models, ensuring 100% data sovereignty and reducing operational costs.

• 𝗣𝗿𝗶𝘃𝗮𝘁𝗲 𝗟𝗟𝗠 𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗦𝗲𝗿𝘃𝗶𝗰𝗲: Deployed optimized open-weights models (Qwen-3) on GPU instances as a secure alternative to GPT-4. Engineered a 𝗖𝘂𝘀𝘁𝗼𝗺 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 𝗔𝗣𝗜 to drive automated script generation and roleplay simulation.

• 𝗛𝗲𝗮𝗱𝗹𝗲𝘀𝘀 𝗖𝗼𝗺𝗳𝘆𝗨𝗜 𝗕𝗮𝗰𝗸𝗲𝗻𝗱: Engineered a scalable image generation service by wrapping complex ComfyUI workflows into 𝗥𝗘𝗦𝗧 𝗔𝗣𝗜𝘀. This enabled the remote production team to trigger consistent character generation programmatically without managing node graphs.

• 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗔𝘀𝘀𝗲𝘁 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲: Integrated 𝗖𝗼𝘇𝘆𝗩𝗼𝗶𝗰𝗲 for high-fidelity TTS and utilized 𝗢𝗻𝗲𝗧𝗿𝗮𝗶𝗻𝗲𝗿 to manage remote LoRA fine-tuning jobs, ensuring strict IP consistency across distributed workflows.

• Tech Stack: Cloud GPU Inference (RunPod/AWS), Headless ComfyUI, API Design, Docker, Python, vLLM/Ollama.

Monolith Decomposition: Decoupled a legacy TypeScript/Node.js GraphQL monolith into independent AWS Lambda microservices, designing robust database schemas to support the new distributed architecture.

Technical Design: Authored detailed Technical Design Docs (TDDs) for major business features, establishing engineering standards and reducing implementation risks for the team.

Performance Optimization: Led/Executed company-wide AWS SDK v2 to v3 migration, utilizing modular imports to reduce Lambda bundle size and cold-start latency.

Production Stability: Diagnosed and resolved critical production issues via deep log analysis and monitoring (CloudWatch), ensuring high availability for the platform.

Infrastructure Consolidation: Migrated 30+ disparate applications into a centralized Kubernetes (K8s) cluster, significantly reducing cloud hosting costs and operational overhead.

DevOps Automation: Containerized (Docker) the entire application portfolio and implemented robust CI/CD pipelines, enabling one-click deployments for the distributed global team.

Strategic Re-platforming: Led the backend migration from Node.js to Python/Django, leveraging Django's built-in admin features to accelerate development cycles and reduce long-term maintenance costs.

Polyglot Development: Refactored and optimized performance-critical microservices using Go (Golang), integrating them seamlessly into the core architecture.

Backend development/architecture

Data pipeline development/architecture