Software engineer focused on AI infrastructure, developer tooling, cloud systems, and applied AI. I am also a licensed Connecticut attorney with a JD, LL.M., and MBA, which makes me especially useful in roles where engineering intersects with legal-tech, compliance, contracts, workflows, risk, or technical customer communication.
Recent work:
- Mastra: contributed to an open-source TypeScript framework for AI agents, workflows, and MCP servers. Built/implemented observability integrations, Azure OpenAI support, MCP auth context propagation, workflow error handling fixes, and CLI/DX improvements.
- Nimbus: created an open-source TypeScript CLI + AWS CDK framework for deploying lightweight NLP models to AWS. Reduces a 35+ step manual deployment process to one command: https://nimbusnlp.github.io/
- RAGCast: built a full-stack RAG app for querying podcast transcripts with OpenAI, LangChain, PostgreSQL/pgvector, and React.
- Mozilla Firefox: contributed WebExtensions API patches, including work related to faster extension security update delivery.
Best fit: Software Engineer, AI Engineer, Backend/Cloud Engineer, DevTools Engineer, Solutions Engineer, or legal-tech / compliance automation roles.
Software engineer focused on cloud infrastructure, developer tooling, and AI-powered applications. Career changer; practiced law for several years before switching to engineering full-time.
Most recently I worked at Mastra, an open-source TypeScript framework for AI agents and workflows. I built the PostHog and Datadog observability exporters bridging OpenTelemetry spans with AI analytics, implemented the Azure OpenAI provider with OAuth2 token caching, and resolved critical state propagation issues in Model Context Protocol (MCP) workflows.
I also created Nimbus (https://nimbusnlp.github.io/), an open-source framework that simplifies deploying lightweight NLP models to AWS. TypeScript CLI + AWS CDK to automate provisioning of serverless infrastructure (Lambda via Docker). Takes what used to be 35+ manual steps down to a single command.
I also built RAGCast, a full-stack RAG application that lets users query and get context-aware answers from podcast transcripts using OpenAI, LangChain, and pgvector.
Previously contributed to Mozilla Firefox, enabling extension developers to manually trigger update checks for faster delivery of security patches, with safeguards to prevent keyboard shortcut conflicts.
Software engineer focused on cloud infrastructure, developer tooling, and AI-powered applications. Career changer; practiced law for several years before switching to engineering full-time.
Most recently I worked at Mastra, an open-source TypeScript framework for AI agents and workflows. I built the PostHog and Datadog observability exporters bridging OpenTelemetry spans with AI analytics, implemented the Azure OpenAI provider with OAuth2 token caching, and resolved critical state propagation issues in Model Context Protocol (MCP) workflows.
I also created Nimbus (https://nimbusnlp.github.io/), an open-source framework that simplifies deploying lightweight NLP models to AWS. TypeScript CLI + AWS CDK to automate provisioning of serverless infrastructure (Lambda via Docker). Takes what used to be 35+ manual steps down to a single command.
I also built RAGCast, a full-stack RAG application that lets users query and get context-aware answers from podcast transcripts using OpenAI, LangChain, and pgvector.
Previously contributed to Mozilla Firefox, enabling extension developers to manually trigger update checks for faster delivery of security patches, with safeguards to prevent keyboard shortcut conflicts.
I'm a software engineer with a background in law and business (JD/MBA), now focused on hands-on engineering for cloud infrastructure, developer tooling, and AI-powered applications. I enjoy architecting reliable systems and have experience with backend development and open-source contributions.
Most recently, I contributed to Mastra, an open-source TypeScript framework for AI agents and workflows. I focused heavily on integrations and reliability: I built the PostHog observability exporter to bridge OpenTelemetry spans with AI analytics, implemented the Azure OpenAI provider with OAuth2 token caching, and resolved critical state propagation issues within Model Context Protocol (MCP) workflows. Working on a Datadog Observability exporter currently.
I also created Nimbus (https://nimbusnlp.github.io/), an open-source framework that simplifies deploying lightweight NLP models to AWS. It utilizes a TypeScript CLI and AWS CDK to automate the provisioning of serverless infrastructure (Lambda via Docker), reducing deployment time from hours to minutes.
Previously, I contributed to Mozilla Firefox, enabling extension developers to manually trigger update checks to accelerate the delivery of security patches, alongside safeguards to prevent keyboard shortcut conflicts.
I also have experience building AI agents and RAG-based applications.
I am looking for remote or hybrid work in NYC or CT.
I'm a software engineer with a background in law and business (JD/MBA), now focused on hands-on engineering for cloud infrastructure, developer tooling, and AI-powered applications. I enjoy architecting reliable systems and have experience with backend development and open-source contributions.
Most recently, I contributed to Mastra, an open-source TypeScript framework for AI agents and workflows. I focused heavily on integrations and reliability: I built the PostHog observability exporter to bridge OpenTelemetry spans with AI analytics, implemented the Azure OpenAI provider with OAuth2 token caching, and resolved critical state propagation issues within Model Context Protocol (MCP) workflows.
I also created Nimbus (https://nimbusnlp.github.io/), an open-source framework that simplifies deploying lightweight NLP models to AWS. It utilizes a TypeScript CLI and AWS CDK to automate the provisioning of serverless infrastructure (Lambda via Docker), reducing deployment time from hours to minutes.
Previously, I contributed to Mozilla Firefox, enabling extension developers to manually trigger update checks to accelerate the delivery of security patches, alongside safeguards to prevent keyboard shortcut conflicts.
I also have experience building AI agents and RAG-based applications.
I am looking for remote or hybrid work in NYC or CT.
Technologies: TypeScript, Node.js, React, AWS CDK/Lambda/API Gateway/S3/ECS, PostgreSQL/pgvector, Docker, OpenTelemetry, Python, OpenAI API, LangChain, Mastra, MongoDB, Ruby
Résumé/CV: Available by request LinkedIn: https://www.linkedin.com/in/richard-loricco-esq/
Email: [email protected]
Software engineer focused on AI infrastructure, developer tooling, cloud systems, and applied AI. I am also a licensed Connecticut attorney with a JD, LL.M., and MBA, which makes me especially useful in roles where engineering intersects with legal-tech, compliance, contracts, workflows, risk, or technical customer communication.
Recent work:
- Mastra: contributed to an open-source TypeScript framework for AI agents, workflows, and MCP servers. Built/implemented observability integrations, Azure OpenAI support, MCP auth context propagation, workflow error handling fixes, and CLI/DX improvements.
- Nimbus: created an open-source TypeScript CLI + AWS CDK framework for deploying lightweight NLP models to AWS. Reduces a 35+ step manual deployment process to one command: https://nimbusnlp.github.io/
- RAGCast: built a full-stack RAG app for querying podcast transcripts with OpenAI, LangChain, PostgreSQL/pgvector, and React.
- Mozilla Firefox: contributed WebExtensions API patches, including work related to faster extension security update delivery.
Best fit: Software Engineer, AI Engineer, Backend/Cloud Engineer, DevTools Engineer, Solutions Engineer, or legal-tech / compliance automation roles.