Principal AI Architect - Remote

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.
• 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.

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.