Senior AI Architect

Role Summary

The Senior AI Architect owns the company’s AI architecture, implementation strategy, and technical delivery standards across internal operations and client-facing systems. This role leads the execution of AI initiatives from solution design through production deployment while managing engineering quality, delivery timelines, and technical alignment across the AI team.

The role is accountable for building scalable AI infrastructure, driving implementation velocity, maintaining deployment reliability, and ensuring the AI team delivers production-ready systems that create measurable operational and commercial impact.

Key Responsibilities (Execution-Focused)

• Define and own the company’s AI architecture strategy across automation systems, AI agents, retrieval pipelines, and operational workflows

• Lead the execution of AI initiatives from discovery through deployment while enforcing delivery timelines, implementation quality, and production standards

• Manage AI engineering execution across active projects including task prioritization, architecture reviews, deployment approvals, and technical decision-making

• Establish engineering standards for AI system design, prompt management, observability, testing, deployment, and documentation

• Architect and deploy production-grade AI systems integrating LLMs, agent frameworks, vector databases, APIs, and workflow orchestration tools

• Review and validate technical implementations to ensure scalability, maintainability, security, and operational reliability before production release

• Drive weekly execution planning with AI developers and engineers to maintain delivery momentum across all implementation initiatives

• Monitor AI infrastructure performance, system uptime, response quality, latency, and operational cost and implement optimization strategies continuously

• Lead integration of AI systems into ERP, CRM, commerce, analytics, and operational platforms

• Translate business requirements into scalable technical roadmaps, implementation phases, and measurable delivery outcomes

• Deliver weekly leadership updates covering implementation progress, deployment risks, resource allocation, and AI roadmap priorities

• Identify operational inefficiencies and deploy AI automation solutions that reduce manual execution and improve business scalability

• Mentor and develop AI engineers through architecture guidance, implementation reviews, troubleshooting support, and performance accountability

• Evaluate emerging AI technologies, frameworks, and deployment methodologies and integrate commercially viable solutions into the company’s AI stack

Role Requirements

Experience & Skills

• 5+ years of experience in software engineering, AI systems development, AI architecture, or technical leadership roles

• Proven experience leading AI engineering projects from architecture through production deployment

• Strong hands-on experience with Python, APIs, cloud infrastructure, distributed systems, and AI application development

• Experience managing engineering execution, delivery standards, and technical implementation quality across multiple projects

• Deep understanding of LLMs, RAG systems, AI agents, orchestration frameworks, vector databases, and semantic retrieval systems

• Experience with frameworks such as LangChain, CrewAI, LangGraph, AutoGen, or equivalent AI orchestration tooling

• Strong understanding of LLMOps, MLOps, CI/CD pipelines, infrastructure monitoring, and deployment automation

• Experience integrating AI systems with ERP, CRM, commerce, analytics, and operational platforms

• Familiarity with Docker, Kubernetes, AWS, Azure, or GCP deployment environments

• Ability to balance implementation speed, scalability, operational reliability, and infrastructure cost

• Experience creating technical standards, architecture documentation, and implementation protocols

• Bachelor’s degree in Computer Science, Software Engineering, AI, or related technical discipline preferred

Mindset & Execution Style

• Operates with ownership across strategy, execution, and deployment outcomes

• Leads engineering execution with clear prioritization, accountability, and measurable delivery expectations

• Makes decisions based on operational impact, scalability, deployment speed, and business value

• Maintains high technical standards while driving rapid implementation cycles

• Escalates blockers early and drives resolution without dependency-heavy management

• Enforces documentation discipline, deployment processes, and implementation consistency across the AI team

• Balances experimentation with production reliability and long-term maintainability

• Maintains strong execution focus while continuously improving systems, workflows, and team performance
  

Success Metrics / KPIs

• AI initiatives delivered within agreed implementation timelines and deployment milestones

• Production AI systems maintain 99%+ uptime and operational reliability

• AI engineering sprint completion and delivery targets achieved consistently

• Reduction in manual operational workload through deployed AI automation systems

• Infrastructure cost, latency, and performance maintained within defined thresholds

• Weekly leadership reporting delivered 100% on time with accurate implementation visibility

• Deployment quality maintained with minimal production issues or rollback incidents

• AI team implementation velocity and deployment throughput improve quarter-over-quarter

Cross-Functional Collaboration

• Collaborates with Leadership to define AI priorities, implementation roadmap, and commercial AI opportunities

• Coordinates with Engineering teams to align AI systems with existing infrastructure and platform architecture

• Works with Operations teams to identify automation opportunities and eliminate execution bottlenecks

• Partners with Product and Commerce stakeholders to implement AI-enabled workflows and customer experiences

• Aligns with DevOps and Infrastructure teams to maintain scalable, secure, and reliable deployment environments

• Provides technical direction and implementation oversight to AI developers, engineers, and external technical partners

Accountability in Cadence

Daily

• Review AI implementation progress, infrastructure health, and deployment execution across active initiatives

• Resolve technical blockers impacting delivery timelines or production reliability

• Monitor AI system performance, automation workflows, and operational KPIs

Weekly

• Lead AI execution planning, architecture reviews, and sprint prioritization sessions

• Deliver leadership updates covering deployment progress, delivery risks, and roadmap status

• Review engineering output and enforce implementation quality standards across projects

Monthly

• Audit AI system performance, infrastructure cost, deployment reliability, and automation impact

• Evaluate AI tooling stack and implement architecture or workflow improvements

• Review team execution performance, delivery throughput, and operational scalability metrics

• Update AI roadmap priorities based on business goals, operational requirements, and deployment learnings