Job Description

Role: AI Engineer

Location: Toronto, CA (Remote)

Type: Contract

Overview:

• We are seeking an Senior AI Engineer to join our team.
• The ideal candidate will have a strong background in implementing AI models, analyzing and improving existing AI architectures, and providing expertise on AI capabilities and systems.
• Person will work closely with our AI team, software engineers, and business stakeholders to create AI-driven solutions that meet our company's business needs and goals.

Responsibilities

• Designing, developing, and implementing AI models
• Analyzing and improving existing AI architectures
• Researching and implementing new AI technologies and methodologies
• Working with data scientists and other stakeholders to understand business needs and goals
• Ensuring the AI systems are aligned with the company's business strategy
• Providing expertise and guidance on AI capabilities and systems
• Ensuring that all AI initiatives follow compliance and regulatory requirements
• Collaborating with other teams to implement and improve AI functionality

Basic Qualifications: (what are the skills required to this job with minimum years of experience on each)

• Minimum 3 to 5 years of experience AI and related domains
• Minimum 3 to 5 years of experience conceptualizing and architecting the target environment for AI solutions
• Minimum 3 to 5 years of experience supporting professional services sales cycle including proposal development, RFP responses, and account expansion initiatives

Degree:

Bachelors in Computer Science or equivalent work experience

Nice to Have; (But not a must)

• Project lead experience with a global team
• Relevant certifications in AI domains

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 metrics

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