Principal AI Architect - Remote

About the job Job Description Principal AI Architect Location: Lombard, IL Remote AI Workflow Solutions Employment Type: Full-time, Remote Job Summary We are seeking a full-time, remote Principal AI Architect to provide enterprise leadership for designing and delivering end-to-end AI, Generative AI, and agentic AI solutions. This role combines hands-on technical expertise with strategic architectural direction, requiring deep expertise in enterprise software engineering, cloud architecture, AI/ML, and Generative AI. The position operates within regulated environments, with a focus on data privacy, security, governance, and responsible AI practices, particularly in healthcare or sensitive-data domains. The role is key in shaping Paradigm’s AI architecture, advancing the AI Center of Excellence (COE), and enabling consistent, governed, and scalable AI solution delivery across the organization. AI Career Training Responsibilities AI Architecture & Solution Delivery: Architect and deliver end-to-end AI, Generative AI, and agentic AI solutions from concept through production. Apply hands-on expertise to build LLM-based systems, RAG pipelines, AI agents, and multi-agent orchestration solutions. Design AI platform capabilities including model selection, LLM routing, retrieval strategies, memory systems, and tool/function orchestration. Lead hands-on prototyping and proof-of-concepts to validate technologies and accelerate adoption. Ensure AI solutions are designed for performance, scalability, observability, privacy, and operational readiness. Define and drive architecture across multiple domains/business segments, ensuring alignment with enterprise strategy. Partner with business and technology leadership to shape AI roadmap, priorities, and execution strategy. Establish and promote architecture standards, reusable patterns, and best practices. Drive modernization initiatives to reduce technical debt and improve scalability, resilience, and performance. Implement and guide AI governance, security, responsible AI, and compliance practices. Collaborate across engineering, data, product, and business teams to deliver production-grade AI solutions. Mentor engineers and architects and effectively communicate AI concepts to technical and non-technical stakeholders. Leadership & Collaboration: Partner with business and technology leadership to define AI strategy, roadmap, and execution priorities. Establish and promote architecture standards, reusable patterns, and best practices. Mentor architects and engineers; build internal capability for AI solution delivery. Communicate complex AI concepts effectively to both technical and non-technical stakeholders. Lead adoption of AI-enabled tools within the team, ensuring effective integration into workflows. Coach employees on appropriate usage, monitor impact on productivity and quality, and identify opportunities for process improvement. Demonstrate a customer-first mindset by developing a broad and deep understanding of Paradigm’s organization, products, operations, and customers. Prioritize collaboration to meet customer needs and take personal accountability for service quality. Technology Strategy & Innovation: Continuously evaluate the evolving AI landscape (LLMs, agents, frameworks, tools). Translate emerging technologies into practical enterprise use cases and capabilities. Drive modernization initiatives to improve scalability, resilience, and performance. Qualifications 10+ years of experience in software engineering, architecture, and enterprise system design. Proven experience delivering end-to-end AI/ML and Generative AI solutions in production environments. Strong hands-on engineering capability with ability to operate across architecture, design, and implementation. Deep expertise in LLMs, prompt engineering, RAG, embeddings, vector databases, and agent-based systems. Experience with agent frameworks and orchestration including multi-agent patterns and integrations. Strong programming experience in Python and building APIs, microservices, and distributed systems. Experience designing and implementing solutions on cloud platforms (Azure preferred). Experience with DevOps practices, including CI/CD, containerization, and scalable deployment of AI systems. Familiarity with infrastructure-as-code (Terraform, Bicep) and Kubernetes-based deployments. Strong understanding of data architecture and integration patterns supporting AI workloads. Ability to evaluate emerging technologies and translate them into enterprise-scale capabilities. Solid knowledge of AI governance, risk, compliance, privacy, and responsible AI principles. Strong communication and stakeholder management skills. Ability to influence decisions across engineering, data, and business teams. Proven ability to mentor, guide, and elevate engineering and architecture teams. Master’s or Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field.