Artificial Intelligence Engineer

Job summary

Design, build, and deploy production-ready AI solutions, including LLM applications, RAG systems, AI agents, and agentic workflows, primarily on GCP and Vertex AI. Develop automation, evaluation, testing, observability, and monitoring capabilities to support the full AI lifecycle and ensure solution quality and performance.

Job details

Duration: 2 months + extensions Location: Remote, GTA required Rate: 50-85/hr Why Open: Expected Vacancy We may use artificial intelligence tools to assist with the screening, assessment, or selection of potential applicants for this position. Required Skills & Experience What You’ll Do Design and build AI and machine learning solutions with a strong focus on production-ready applications. Develop LLM-powered applications, RAG systems, and AI agents. Build agentic AI workflows using LangGraph. Build automation around AI and ML workflows. Develop and deploy AI solutions in the cloud, primarily using GCP and Vertex AI. Use Python for AI/ML application development. Develop evaluations, testing frameworks, and monitoring to measure the quality and performance of AI solutions. Leverage Langfuse for observability and evaluation of LLM applications. Work across traditional machine learning, generative AI, and reinforcement learning use cases. Support the end-to-end AI lifecycle, including development, deployment, evaluation, testing, and monitoring. What We’re Looking For 3–5 years of experience in machine learning, AI engineering, software engineering, or a related field. Strong Python development experience. Hands-on experience building LLM applications and AI agents. Experience designing and implementing Retrieval-Augmented Generation (RAG) systems. Experience with LangGraph or similar agent orchestration frameworks. Experience building AI/ML solutions on GCP, particularly Vertex AI. AWS experience is also relevant. Understanding of LLM evaluation, observability, testing, and monitoring, ideally including experience with Langfuse. Experience deploying AI/ML solutions into production. Exposure to traditional ML and reinforcement learning is an asset. Ability to think beyond the model itself and build reliable, scalable AI products. Job Description Insight Global is looking for an AI Engineer to help build and scale AI-powered solutions supporting media insights, advertising campaigns, data analytics, and business intelligence. This individual will be responsible for taking AI solutions from concept through production, including building LLM applications, RAG systems and AI agents, deploying solutions in the cloud, and developing the evaluation and monitoring frameworks needed to ensure those solutions perform effectively.

What you’ll do

Design, build, and deploy production-ready AI solutions, including LLM applications, RAG systems, AI agents, and agentic workflows, primarily on GCP and Vertex AI. Develop automation, evaluation, testing, observability, and monitoring capabilities to support the full AI lifecycle and ensure solution quality and performance.

Requirements

Requires 3–5 years of relevant experience and strong Python skills, with hands-on experience building LLM applications, AI agents, and RAG systems. Candidates should have experience with agent orchestration frameworks such as LangGraph, cloud AI deployment—especially GCP and Vertex AI—and production evaluation and monitoring; AWS, traditional machine learning, and reinforcement learning are also relevant.

Listed skills

• Python · Preferred
• Machine learning · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Python
• Machine Learning
• Artificial Intelligence
• Large Language Models
• Retrieval-Augmented Generation
• AI Agents
• LangGraph
• Google Cloud Platform
• Vertex AI
• LLM Evaluation
• Langfuse
• AI Observability
• Production Deployment
• Reinforcement Learning
• Cloud Computing

Job areas

• Technology
• Software
• Data & Analytics
• Engineering
• Retail

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