AI/ML Senior Technical Architect(Salesforce experience is a must)

About the Role

Strategist Mindset

Orchestrator Mindset

Builder Mindset

Required Qualifications

• 13+ 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.
• 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.

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.

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.