Lead Specialist, AI Solution Architect

Responsibilities

• Architect and lead end-to-end delivery of enterprise-scale AI solutions using Agile and DevOps practices, providing technical leadership across planning, development, code quality, reviews, and release management with tools such as Azure DevOps, JIRA, Git, and CI/CD pipelines.
• Design and implement secure, resilient, and scalable cloud-native architectures on Microsoft Azure, leveraging IaaS/PaaS services, modern application stacks, and enterprise data platforms to meet regulatory and performance requirements.
• Drive AI, GenAI, and agent-based solution strategy by leading proofs of concept and pilots, and guiding successful initiatives through transition into production-ready, operational platforms.
• Translate complex business needs into pragmatic technical solutions by partnering with business stakeholders, product owners, and enterprise architects to shape roadmaps, evaluate emerging technologies, and align AI capabilities to measurable business outcomes.
• Design and enable advanced agentic and intelligent automation patterns, including retrieval-augmented generation (RAG), multi-agent and A2A architectures, orchestration, state management, observability, and interoperable workflows that scale beyond pilot stages.
• Establish and enforce standards for architecture artifacts, documentation, deployment patterns, monitoring, and support while embedding security, privacy, and responsible AI principles into all solution designs; mentor and guide onshore and offshore engineering teams to foster a high-performance, innovation-driven culture.

Qualifications

• Minimum of 5 years of experience designing and leading enterprise-scale technology solutions, with demonstrated ownership of architecture and delivery in complex environments.
• Bachelor's degree from an accredited college or university in Computer Science, Engineering, or a related field is required.
• Strong background in cloud-native architecture on Microsoft Azure, including application services, data platforms, integration patterns, and DevOps automation.
• Proven expertise in AI-driven and modern data architectures, including GenAI, agent-based systems, and RAG-style solutions, with experience taking concepts through production deployment.
• Solid understanding of full-stack systems, including modern web frameworks, backend services, APIs, and data integrations, with the ability to make pragmatic architectural trade-offs.
• Demonstrated experience leading and mentoring technical teams, influencing senior stakeholders, and serving as a trusted technical advisor with strong written and verbal communication skills.
• Ability to travel as required.