AI/ML Developer

What you’ll bring

5+ years experience in hands-on software development of highly technical products.

Past experience in Software Development or other similar hands-on development roles at or above the level of Senior Software Engineer.

Experience crafting and tailoring a message for potential customers, including executives.

Degree in Computer Science or related field, or equivalent professional experience.

Skill Requirements:

AI-related experience (experience developing code based AI agents such as conversational AI agents a plus).

Experience with React, Typescript, and Go.

Past roles that interface with end customers to influence software development.

Engineers working on building AI agents should be self-sufficient engineers at a senior level. They should be completely comfortable with normal software development lifecycles and be able to write code independently, including building components, integrating with APIs, and testing/deployment

The AI agent is built programmatically using code. It is explicitly NOT a no-code/low-code application. Everything including setup, permissions, version control, development, testing, and deployment are done via extensive coding workflows.

Engineers should be highly experienced in ReactJS and Typescript, comfortable with VCS using Git, and other standard engineering workflows. Should have a working understanding of LLMs and writing API integrations in Typescript.

Engineers should have a comprehensive understanding of React JS, especially core concepts/philosophies of developing with React and front-end frameworks in general. The Agent SDK has some important distinctions from React, but understanding of React development lifecycle and component lifecycle will enable engineers to grasp these differences and develop against them.

Most engineers will primarily need to know

React (Intermedia to Advanced)

Typescript (Intermedia to Advanced )

LLM/prompt engineering basics

Github/version control

Terminal

Typical AI Engineer Role and Responsibility

build the agent from scratch using product requirements

This includes developing the agent, testing different scenarios including knowledge retrieval and support journeys, and writing simulation tests

This can (should) also include interfacing directly with the client in a product capacity, ensuring that the agent is meeting their needs and can connect to all necessary APIs/databases to do its job

Review live conversations and client feedback, using the experience manager

This includes identifying negative conversations using evaluation tools, creating issues for bad conversations, receiving issues from the client, and so on

This is the steady state of the agent -- regularly checking in, making improvements, evaluating me