Principal AI Architect - Remote

Paradigm•Lombard, IL

3d•Remote

About The Position

We are seeking a full-time, remote Principal AI Architect. The Principal AI Architect role provides enterprise leadership for the design and delivery of end-to-end AI, Generative AI, and agentic AI solutions, while also contributing hands-on technical expertise to prototyping, implementation, and architectural direction. This position requires deep expertise in enterprise software engineering, cloud architecture, AI/ML, and Generative AI, with responsibility for translating evolving AI capabilities into scalable, secure, and production-ready solutions. The role operates within regulated environments and is accountable for incorporating data privacy, security, governance, and responsible AI practices into solution design and delivery, particularly in healthcare or other sensitive-data domains. This position plays a key role 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.

Requirements

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 STEM field.

Responsibilities

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
Ensure AI solutions are designed for performance, observability, privacy, and operational readiness
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
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.
Leads adoption of AI-enabled tools within the team, ensuring effective integration into workflows.
Coaches employees on appropriate usage, monitors impact on productivity and quality and identifies opportunities for process improvement.
Demonstrates a customer-first mindset by developing a broad and deep (where appropriate) understanding of Paradigm organization, products, operations, and customers.
Prioritizes collaboration to meet customer needs and expectations and takes personal accountability for service quality.
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.