• Building a Multi-Agent Marketing Orchestrator using Fetch.ai’s uAgents framework — automating end-to-end marketing workflows for small businesses.

• Working with tools like CrewAI, LangGraph, and ASI-1 Mini to design and prototype intelligent agents for content generation, competitor research, and campaign scheduling.

• Collaborating with Fetch.ai’s marketing and product teams to understand real-world pain points, refine agent design, and prepare for deployment on Agentverse.

• Led the end-to-end development of a containerized experimentation environment using Amazon SageMaker, Docker, ECR, and S3 — replacing fragmented local setups with a fully reproducible, cloud-based alternative.

• Reduced setup time from many hours (or even days) to just minutes, enabling faster iteration and collaboration across teams.

• Drove the full product lifecycle — from gathering user and system requirements, designing system architecture, implementing infrastructure, to writing detailed SOPs and onboarding scientific users.

• Collaborated closely with data scientists and engineering stakeholders to validate use cases, incorporate feedback, and ensure seamless adoption across the org.

• This experience gave me hands-on exposure to building platform-level tooling at scale and reinforced my interest in solving foundational engineering problems that directly impact scientific productivity.

• Evaluated and selected best responses from over 500 AI-generated outputs from Large Language Models (LLMs), enhancing model accuracy by 30% through targeted feedback.

• Conducted qualitative assessments of AI-generated content to ensure high relevance and usability, contributing to the overall improvement of the AI’s performance and user experience.

• Developed from scratch an in-house data aggregation and visualization application suite, leading to a 30% improvement in data insights and usability for over 500 employees.

• Implemented API encryption and designed a secure user token system for backend, overseeing entire development process from initial setup to deployment.

• Utilized Python FastAPI, Celery, Ruby on Rails, PostgreSQL, SQLAlchemy ORM, and ReactJS to engineer scalable backend systems and dynamic frontend applications, supporting 500+ transactions daily.

• Created and optimized query-heavy and visualization APIs in Ruby for an admin-facing Ruby on Rails backend, improving efficiency by 75%.

◦ Refactored legacy code to Node.js 14 and integrated with REST APIs.

◦ Developed and maintained a Chromium browser extension to enhance user functionality and performance.

◦ Built the UI for a browser extension using VueJS to deliver a responsive and intuitive user experience.

◦ Implemented GitLab CI/CD pipelines for DevOps automation to streamline deployment and improve continuous integration.