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!

• Work with enterprise customers and internal teams to turn business workflows into scalable, production-ready agentic AI systems. • Design and build LLM-powered agents that reason, plan, and act across tools and data sources with enterprise-grade reliability. • Balance rapid iteration with enterprise requirements, evolving prototypes into stable, reusable solutions. • Define and apply evaluation and quality standards to measure success, failures, and regressions. • Debug real-world agent behavior and systematically improve prompts, workflows, tools, and guardrails. • Contribute to shared frameworks and patterns that enable consistent delivery across customers.

• Recommend and develop comprehensive multi-agent systems and enterprise AI systems for complex applications and products. • Pitch concepts to team leadership and lead construction of prototypes and minimum viable products to validate AI/ML solutions before committing substantial resources. • Assist in designing and implementing the cloud architecture of large multi-faceted AI systems on Kubernetes using platforms like Azure AI Foundry with comprehensive agent governance. • Assist in the design and scaffolding of robust data pipelines that drive large complex AI systems including graph databases, tensor-based fusion, and multi-model retrieval. • Develop specifications for enterprise-wide agent communication standards, tool governance, and evaluation harnesses to deploy AI models at scale. • Develop the code for monitoring models and AI systems including customer evaluation metrics, agent behavior analytics, and comprehensive drift detection that ensures consistent and reliable performance. • Actively participate in a team that exercises principled, agile-like development practices. • Create and maintain thorough documentation that is consistent with team procedures, corporate policies, and expectations including architectural decision records. • Ensure peer review on all assigned work, as well as conduct peer reviews over the work of others as requested. • Guide and mentor junior AI engineers on AI/ML and industry best practices and methodologies and emerging standards like A2A, MCP, and novel reasoning/retrieval techniques. • Keep abreast of new tools and concepts through reading documentation or literature and actively practicing skills development. • Advise team and division leadership on matters such as enterprise AI strategies, AI related technology strategies and roadmaps, AI related infrastructure strategies, and roadmaps. • Meet and collaborate with external stakeholders to conceptualize AI solutions that realize business value while ensuring AI governance adherence, AI best practices, and data quality. • Provide thought leadership to the Premera AI community.