Jobs in Mississauga, United States

Location: Toronto, CA (Remote)

What you'll do

1. Build enterprise-grade AI agents: As an Agent Engineer, you will be responsible for partnering directly with our CX team to build and ship AI agents that handle thousands of customer conversations a day.

What you’ll bring

1. 3+ years experience in hands-on software development of highly technical products.
2. Past experience in Software Development or other similar hands-on development roles at or above the level of Senior Software Engineer.
3. Experience crafting and tailoring messages for potential customers, including executives.
4. Degree in Computer Science or related field, or equivalent professional experience.

Skill Requirements:

1. AI-related experience (experience developing code-based AI agents such as conversational AI agents a plus).
2. Experience with React, Typescript, and Go.
3. Experience interfacing with end customers to influence software development.
4. Engineers working on building AI agents should be self-sufficient at a senior level, familiar with software development lifecycles, capable of writing code independently, including building components, integrating with APIs, and testing/deployment.
5. The AI agent is built programmatically using code, involving extensive coding workflows for setup, permissions, version control, development, testing, and deployment.
6. Strong experience in ReactJS and Typescript, comfortable with Git and other engineering workflows.
7. Understanding of LLMs and API integrations in Typescript.
8. Deep understanding of React development lifecycle and component architecture to develop against the Agent SDK, which has distinctions from React.

Most engineers should know:

• React (Intermediate to Advanced)
• Typescript (Intermediate to Advanced)
• LLM/prompt engineering basics
• Git/version control
• Terminal usage

Typical AI Engineer Responsibilities:

1. Build the agent from scratch based on product requirements.
2. Develop, test various scenarios including knowledge retrieval and support journeys, and write simulation tests.
3. Interface directly with clients to ensure the agent meets their needs and connects to necessary APIs/databases.
4. Review live conversations and client feedback, identify negative interactions, create issues, and implement improvements.
5. Regularly evaluate metrics and enhance the agent's performance in steady state.