I'm an AWS-certified ML Engineer experienced in building end-to-end solutions from hands-on ML/DevOps to strategic planning from my INSEAD MBA experience. What I offer: - End-to-end ML orchestration: Designs, manages, and optimizes the full ML lineage to ensure scalable and reproducible production environments. - Strategic technical alignment: Identifies technical "sweet spots" where investment generates the highest business ROI. - Rapid zero-to-one delivery: Translating ambiguous business challenges into technical frameworks and executing high-impact MVPs. Achievements so far: - Reached 60K Python SDK downloads for autonomous AI agent network (competitor: LangChain) - Architected the core AI for personalized learning platform with 100+ mentors. - Led the deployment of scalable gaming SDKs and scaled the division to US$4M in annual revenue.…

Kuriko I

I'm an AWS-certified ML Engineer experienced in building end-to-end solutions from hands-on ML/DevOps to strategic planning from my INSEAD MBA experience. What I offer: - End-to-end ML orchestration: Designs, manages, and optimizes the full ML lineage to ensure scalable and reproducible production environments. - Strategic technical alignment: Identifies technical "sweet spots" where investment generates the highest business ROI. - Rapid zero-to-one delivery: Translating ambiguous business challenges into technical frameworks and executing high-impact MVPs. Achievements so far: - Reached 60K Python SDK downloads for autonomous AI agent network (competitor: LangChain) - Architected the core AI for personalized learning platform with 100+ mentors. - Led the deployment of scalable gaming SDKs and scaled the division to US$4M in annual revenue.…

Available to hire

I’m an AWS-certified ML Engineer experienced in building end-to-end solutions from hands-on ML/DevOps to strategic planning from my INSEAD MBA experience.

What I offer:

  • End-to-end ML orchestration: Designs, manages, and optimizes the full ML lineage to ensure scalable and reproducible production environments.
  • Strategic technical alignment: Identifies technical “sweet spots” where investment generates the highest business ROI.
  • Rapid zero-to-one delivery: Translating ambiguous business challenges into technical frameworks and executing high-impact MVPs.

Achievements so far:

  • Reached 60K Python SDK downloads for autonomous AI agent network (competitor: LangChain)
  • Architected the core AI for personalized learning platform with 100+ mentors.
  • Led the deployment of scalable gaming SDKs and scaled the division to US$4M in annual revenue.
See more

Skills

Experience Level

AWS
Expert
Data Science
Expert
ML
Expert
Python
Expert

Language

English
Fluent

Work Experience

CTO at Version IO
August 13, 2024 - Present
- CTO & Lead Architect of versionHQ, a Python framework for autonomous agent networks; grew the user base to 60K+ SDK downloads. - Engineered a dynamic task graph system using Graph Theory (NetworkX) to automate complex reasoning; implemented automated formation logic (Solo, Supervising, Squad) to optimize agent collaboration. - Built a model-agnostic AI infrastructure integrating LiteLLM, Mem0, and ChromaDB, allowing seamless orchestration across various LLMs including Gemini and GPT. - Secured $200K in AWS Bedrock/SageMaker credits, leveraging the funding to transition the framework from a zero-to-MVP state to a scalable production environment.

Education

Add your educational history here.

Qualifications

AWS Certified ML Engineer
February 1, 2025 - December 19, 2025
Validates technical ability in implementing ML workloads in production.
AWS Certified ML Specialty
February 1, 2025 - December 19, 2025
Demonstrated ability to build, train, tune and deploy ML models on AWS.

Industry Experience

Education, Gaming, Media & Entertainment, Software & Internet, Telecommunications, Retail
    paper Dynamic Pricing System (MLOps & Serverless MVP)

    A production-grade ML system to democratize competitive pricing strategies for mid-sized retailers through real-time demand forecasting and automated price optimization.

    Core Impact & Technical Achievements:

    • Serverless inference engine as event-driven microservice using AWS Lambda and API Gateway.

    • Integrated AWS ElastiCache (Redis) to serve cached historical data and predictions.

    • Hybrid deep learning pipeline: Developed a primary PyTorch model optimized via Optuna (Bayesian Optimization) with an automated failover mechanism to backup models (LightGBM, SVR, Elastic Net) for 100% availability.

    • Engineered a weekly-scheduled pipeline using Prefect and DVC to manage the full ML lifecycle.

    • Implemented automated Data Drift (Evently AI) and Fairness/Bias testing (SHAP).

    • Established a robust CI/CD pipeline using GitHub Actions and AWS CodeBuild, incorporating Snyk for security scanning (SAST/SCA) and mandatory human-in-the-loop review for production deployments.

    Technical Toolkit:

    • Machine Learning: PyTorch, LightGBM, Optuna, Scikit-learn, SHAP, Bayesian Optimization.
    • Cloud (AWS): Lambda, ECR, API Gateway, S3, ElastiCache (Redis), CodeBuild.
    • MLOps/DevOps: DVC, Prefect, Docker, GitHub Actions, Snyk, uv (Package Management).

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