About the Role

The AI Technical Architect operates at the intersection of strategy and execution — acting as both a Strategist, shaping the long-term technical vision for AI adoption, and an Orchestrator, coordinating people, systems, and processes to turn that vision into working solutions. This role is central to designing, scaling, and governing AI and Agentforce-driven architectures across the delivery organization, ensuring technical decisions align with business outcomes and customer value.

Key Responsibilities

Strategist Mindset

• Define and evolve the technical architecture strategy for AI initiatives, aligning with broader business and platform roadmaps.

• Evaluate emerging AI technologies (LLMs, agentic frameworks, automation platforms) and recommend where they create differentiated value.

• Partner with delivery, product, and engineering leadership to translate business goals into scalable, secure, and maintainable AI architectures.

• Establish architectural standards, patterns, and best practices for AI solution design across teams.

• Assess technical risk, feasibility, and total cost of ownership for proposed AI initiatives.

Orchestrator Mindset

• Coordinate cross-functional teams (engineering, data, product, delivery) to ensure AI solutions are designed, built, and deployed cohesively.

• Serve as the technical glue between strategy and execution — ensuring architecture decisions are implemented consistently across squads and projects.

• Facilitate technical governance forums, architecture reviews, and design decision checkpoints.

• Drive alignment between multiple parallel AI workstreams, resolving dependencies and technical conflicts.

• Mentor and enable technical teams and solution architects on AI-first design principles.

Delivery & Governance

Builder Mindset

• Ship working AI solutions hands-on — prototype, build, and iterate quickly rather than only designing on paper.

• Take ideas from concept to production, personally building agents, automations, and integrations when needed to prove value fast.

• Champion a bias for action: favor working demos and MVPs over long design documents.

• Continuously experiment with new AI tools and techniques, bringing working proofs-of-concept back to the team.

• Balance speed of shipping with architectural soundness, ensuring builds are scalable beyond the prototype stage.

• Own end-to-end technical quality of AI solutions from design through production readiness.

• Define and monitor architecture health metrics (scalability, reliability, cost-efficiency, security posture).

• Ensure compliance with data governance, responsible AI, and security standards across all AI implementations.

• Provide technical escalation support for complex AI/architecture issues across the portfolio.

Required Qualifications

• 10+ years of experience in software/solution architecture, with demonstrated depth in AI/ML or agentic systems architecture.

• Proven experience architecting and delivering enterprise-scale AI solutions (e.g., LLM-based systems, automation platforms, Agentforce, or equivalent).

• Strong grasp of cloud-native architecture, integration patterns, and platform scalability principles.

• Demonstrated ability to operate as both a strategic thinker and hands-on technical orchestrator across multiple teams.

• Experience with Salesforce platform architecture is highly valued.

• Excellent stakeholder management and communication skills — able to translate technical concepts for both technical and non-technical audiences.

Preferred Qualifications

• Salesforce Certified Technical Architect (CTA) or equivalent architecture certification.

• Experience leading AI governance, responsible AI, or AI risk frameworks.

• Track record of mentoring architects and senior engineers.

• Exposure to multi-org, multi-cloud, or complex enterprise topology environments.

• Hands-on experience building and deploying AI agents (e.g., Agentforce, custom agent frameworks) in real-world settings.

• Practical experience with AI tools such as Gemini, Cursor, and Claude for coding, prototyping, or workflow automation.

• Familiarity with prompt engineering, LLM orchestration, and agentic tool-use patterns.

• Comfortable working directly in code/config to build and ship — not purely a design-and-document architect.