Summary
✨ AI‑Generated
A mid-level AI engineering position focused on designing, deploying, and improving machine learning and generative AI capabilities. The role includes building reliable services, pipelines, and AI-powered user experiences.
Highlights
Opportunity to build production AI systems with significant ownership and work at the intersection of machine learning, intelligent agents, and product development.
Description
Jobright is a personal AI job search agent that simplifies job searching and helps users find better job outcomes.
The Mid-Level AI Engineer designs, builds, evaluates, deploys, and operates machine learning and generative AI capabilities, including model-integrated services, data pipelines, and production systems.
Why Join Us
• Build real, production AI agents used by real users
• High ownership and impact
• Work at the intersection of AI, agents, and product
• Shape how people experience AI-driven job search
Responsibilities
• Design and implement machine learning and generative AI features
• Build evaluation, monitoring, and data pipelines for AI systems
• Develop APIs and services that integrate models into products
• Improve model quality, latency, reliability, and cost
• Partner with product and engineering teams on technical decisions
• Review code, document systems, and support production operations
• Evaluate new models, frameworks, and techniques against product needs
Qualification
Required
• 3 or more years of professional software or machine learning engineering experience
• Strong Python programming and software engineering fundamentals
• Experience building or deploying machine learning systems
• Familiarity with LLMs, retrieval, evaluation, or model-serving workflows
• Experience with cloud platforms, APIs, data pipelines, and testing
• Strong communication and independent problem-solving skills
• Authorised to work in Canada
Preferred
• Experience with PyTorch, TensorFlow, vector databases, or orchestration frameworks
• Experience monitoring production AI systems
• Familiarity with experimentation, ranking, search, or recommendation systems