• Architect and implement Generative AI features leveraging Large Language Models (LLMs), RAG frameworks, and agent orchestration. • Design scalable systems translating complex product requirements into modular, cloud-native software. • Contribute to the continuous evolution of our platform using Java, Kafka, and cloud-native best practices (AWS/Azure). • Mentor junior engineers, conduct deep-dive code reviews, advocate for high-quality, testable, and maintainable source code. • Collaborate in cross-functional agile squads to launch production-ready applications solving real-world transportation challenges.

• Architect and implement the generative intelligence core of our upcoming project • Design the RAG (Retrieval-Augmented Generation) architectures • Select the appropriate model stacks • Ensure AI outputs are grounded, safe, and performant • Work in lockstep with the Technical Leader to integrate AI services • Mentor the team on AI engineering best practices

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description We are building the most advanced AI Agent for the Web3 industry, leveraging the largest proprietary dataset in the field. We seek a core algorithm engineer to architect AI Agent systems, optimize end-to-end RAG pipelines, implement LLM training/alignment, and deploy scalable. Core Responsibilities - Develop AI Agent Systems: Build intelligent search and task execution agents using ReAct, planning, and multi-agent frameworks (e.g., LangGraph, Dify, CrewAI) - Optimize End-to-End RAG Pipelines: Build and refine efficient RAG systems from ingestion, chunking, and embedding to hybrid vector search (OpenSearch), implementing precise grounding and citation - LLM Training & Alignment: Conduct advanced post-training (SFT, RLHF, continual pretraining) and align models for reliable JSON-schema function calling and external tool usage - Automated Evaluation & Iteration: Build offline/online evaluation pipelines using synthetic QA, retrieval metrics, and hallucination detection to continuously improve system performance and stability Qualifications - Bachelor's or Master's degree in Computer Science, AI, Machine Learning, or a related field - 3+ years of experience developing AI systems, with a focus on RAG, Agent architectures, or LLM training/optimization - Proficiency in Python and key ML frameworks (PyTorch/TensorFlow), with experience in distributed training and high-performance inference - Hands-on, in-depth experience in at least two of the following domains: - End-to-end RAG pipeline development and optimization with OpenSearch/vector databases - AI Agent framework development (LangGraph, CrewAI, ReAct) - Advanced LLM training (SFT, RLHF, LoRA) and alignment techniques - Excellent problem-solving and systems thinking skills. Passion for Web3 and AI is a plus Key Outcomes - Deliver a high-accuracy, low-latency AI Agent system to power intelligent Web3 applications - Achieve continuous improvement in RAG retrieval accuracy and establish an automated evaluation and iteration loop - Drive LLM performance optimization

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description The AI Engineer – Autonomous Agent will work closely with the Product and Engineering teams to design and implement agent-based solutions that align with product goals. The AI Engineer – Autonomous Agent will also build new agent data types and pipelines and enable frameworks and workflows for agent reasoning, function calling, and action coordination. Key Responsibilities - Collaborate with product and engineering teams to translate product objectives into autonomous agent-based solutions. - Design and build new agent data types, and pipelines that enable agents frameworks, and workflows to coordinate reasoning, function calling, actions. - Develop and optimize autonomous agents leveraging LLMs, planning algorithms, and multi-step reasoning approaches. - Implement comprehensive security mechanisms for autonomous agents, including authentication, role-based access control, audit logging, and compliance monitoring. - Integrate agents securely with existing systems, APIs, and data sources with secure communication protocols. Qualifications - Expert level in Python, frameworks, and functional programming principles. - Experience designing and implementing LLM agent workflows, and familiarity with frameworks such as LangChain, LlamaIndex, LangGraph, CrewAI, and AutoGen. Preferred - Master’s degree or higher in Artificial Intelligence. - Experience implementing security at scale including role-based access control, multi-factor authentication, network security best practices, and compliance monitoring. Requirements - Travel to Office expectations: - For Remote Roles: If this role is remote, there will be in-office events that will require travel to and from the Mississauga and/or Salt Lake City office. These will include, but not limited to, onboarding, team events, semi-annual and annual team meetings. - For Hybrid Roles: If this role is Hybrid, there will be an expectation to reside within commutable distance to the office/location specified in the job listing. This will include, but not limited to, weekly/bi-weekly/monthly events in the office with your specific team. This is a requirement for this role. Benefits - Benefits starting from Day 1! - Retirement Plan Matching - Flexible Paid Time Off - Wellness Support Programs and Resources - Parental & Caregiver Leaves - Fertility & Adoption Support - Continuous Development Support Program - Employee Assistance Program - Allyship and Inclusion Communities - Employee Recognition … and more!