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.

Role Description The Full-Stack AI Engineer (Remote) is responsible for designing, developing, deploying, and maintaining enterprise-grade AI and machine learning (ML) solutions that integrate seamlessly across the company’s technology ecosystem, including Microsoft Copilot Studio, Power Platform, and AWS-based ML environments. This hybrid role bridges AI/ML development, agentic AI orchestration, and MLOps infrastructure, ensuring that models and intelligent agents are secure, scalable, and business-aligned. The position advances the company’s use of AI across all internal and external customer experiences by: - Building and operationalizing AI models and copilots that augment business workflows. - Integrating models into Microsoft Copilot Studio, Power Apps, and Power Automate. - Managing the lifecycle of models deployed through AWS SageMaker, Azure ML, or internal APIs. Qualifications - 5–8 years combined experience in software engineering, ML, and/or MLOps. - Proven success deploying AI/ML models in Microsoft or AWS environments. - Strong proficiency in Python, ML frameworks (Scikit-learn, PyTorch, TensorFlow), and API integration. - Hands-on experience with Microsoft Copilot Studio, Power Automate, and Power Apps development. - Experience deploying AI pipelines in AWS SageMaker or Azure ML. - Familiarity with LLM-based copilots, agent orchestration, and prompt engineering. Requirements - Design, fine-tune, and deploy AI/ML models, including LLMs and retrieval-augmented generation (RAG) pipelines. - Develop and maintain custom Copilots and intelligent agents within Microsoft Copilot Studio that interact with enterprise data and APIs. - Implement Power Platform integrations (Power Apps, Power Automate, Power Virtual Agents) for business-facing AI workflows. - Collaborate with data scientists to transform prototypes into production-grade, secure, and compliant solutions. - Build domain-specific AI skills and agents tailored to insurance and financial processes. - Develop and automate ML pipelines for training, deployment, and monitoring on AWS SageMaker and Azure ML. - Manage CI/CD for AI models and Copilot plugins using MLflow, Airflow, Docker, and Kubernetes. - Implement monitoring for Copilot-based and standalone AI agents, capturing performance, drift, and usage analytics. - Integrate AI governance frameworks ensuring auditability, explainability, and data privacy compliance. - Partner with software engineering and Power Platform teams to embed AI services into core business systems. - Collaborate with IT and Security on Copilot extensions and secure connectors for enterprise data. - Mentor junior AI engineers on cloud AI tools, Copilot integration, and MLOps best practices. Preferred - Experience connecting Copilot agents to proprietary datasets via secure connectors or APIs. - Knowledge of vector databases (Pinecone, FAISS) and RAG architectures. - Familiarity with Azure OpenAI Service, Dataverse, and Microsoft Graph APIs. - Background in financial or insurance domain AI use cases. Education - Bachelor’s or Master’s degree in Computer Science, Data Science, or a related technical field. Certifications (Preferred) - Microsoft Certified: Azure AI Engineer Associate (AI-102) - Microsoft Certified: Power Platform Solution Architect / Developer Associate - AWS Certified Machine Learning – Specialty - Microsoft Copilot Studio or AI Builder training credentials Physical Requirements Representative of those that must be met by an employee to successfully perform the essential function of the job. Must be able to operate a PC, other relevant office tools/equipment, sit for extended periods, and/or occasionally stand for extended periods, lead training classes and travel. Reasonable accommodation may be made to enable individuals with disabilities to perform the essential functions. Compensation Compensation offered for this role is $100,000.00 - $177,150.00 annually and is based on experience and qualifications. Candidates must possess authorization to work in the United States, as it is not our practice to sponsor individuals for work visas. Answer Financial is an equal opportunity employer.