New opportunity

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

Salesforce · Bangalore, India

About this role

The AI Technical Architect at Salesforce operates at the intersection of strategy and execution, serving as both a strategist for long-term AI vision and an orchestrator for cross-functional delivery. This role is responsible for designing, scaling, and governing AI and Agentforce-driven architectures to ensure alignment with business outcomes. Key responsibilities include defining technical strategy, evaluating emerging AI technologies, establishing architectural standards, and coordinating cross-functional teams. The role requires a 'builder' mindset, involving hands-on prototyping, building agents and automations, and ensuring production readiness. Responsibilities: Define and evolve technical architecture strategy for AI initiatives. Evaluate LLMs, agentic frameworks, and automation platforms. Coordinate engineering, data, and product teams to ensure cohesive deployment. Facilitate technical governance and architecture reviews. Build and ship working AI solutions, MVPs, and demos. Monitor architecture health metrics including scalability, reliability, and security. Required Qualifications: 13+ years of experience in software/solution architecture with depth in AI/ML or agentic systems. Proven experience delivering enterprise-scale AI solutions. Strong grasp of cloud-native architecture and platform scalability. Ability to operate as a strategic thinker and hands-on technical orchestrator. Salesforce platform architecture experience is highly valued. Excellent stakeholder management and communication skills. Preferred Qualifications: Salesforce Certified Technical Architect (CTA) or equivalent. Experience leading AI governance or risk frameworks. Track record of mentoring architects. Exposure to complex enterprise topologies. Hands-on experience with AI agents and tools like Gemini, Cursor, and Claude.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Software ArchitectureSolution ArchitectureAI/MLAgentic SystemsLLMAgentforceCloud-native ArchitectureSalesforce PlatformTechnical GovernancePrompt EngineeringAI OrchestrationGeminiCursorClaude

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Orchestrating Cross-Functional AI Delivery
The role requires acting as the technical glue between engineering, data, and product teams. You must prioritize aligning disparate workstreams, resolving technical dependencies, and ensuring that AI architectures are implemented consistently across multiple squads while maintaining a focus on business outcomes.
Hands-on Prototyping and Production Scaling
Beyond high-level strategy, you must maintain a 'builder' mindset. This involves personally prototyping agents and automations to prove value quickly, then scaling those MVPs into production-ready, secure, and reliable enterprise-grade architectures.
Establishing AI Governance and Standards
You are responsible for defining architectural standards and facilitating technical governance. This includes assessing technical risk, ensuring compliance with responsible AI frameworks, and monitoring architecture health metrics like scalability and cost-efficiency.

Likely challenges to think through

Balancing Strategic Vision with Rapid Execution
The primary tension lies in the requirement to be both a long-term strategist and a hands-on builder. Prepare to discuss how you maintain architectural integrity while favoring a 'bias for action' and rapid prototyping, specifically avoiding the trap of over-designing versus shipping working demos.
Navigating Complex Salesforce Ecosystems
Given the requirement for deep Salesforce platform experience, you may face challenges integrating custom AI agents with existing, complex enterprise topologies. Prepare to demonstrate how you handle multi-org or multi-cloud integration patterns while ensuring the AI solutions remain maintainable and secure.

Practice your answers

Technical practice

Designing Scalable Agentic Architectures

What to coverExplain your approach to designing agentic systems that remain performant and reliable at enterprise scale. Discuss how you handle state management, tool-use patterns, and latency trade-offs when integrating LLMs into complex Salesforce environments.

Evaluating LLM and Framework Suitability

What to coverDescribe your methodology for evaluating and selecting LLMs or agentic frameworks for specific business use cases. Address how you balance model capability, cost, security, and the specific requirements of the Salesforce platform.

AI Governance and Risk Frameworks

What to coverHow do you implement technical governance for AI solutions to ensure compliance, security, and responsible AI practices? Discuss your experience in defining guardrails for data privacy and mitigating risks like hallucinations or prompt injection.

Integrating AI with Salesforce Platform

What to coverGiven the requirement for Salesforce experience, how do you architect AI solutions that leverage existing Salesforce data models and security structures? Explain how you ensure seamless integration between Agentforce or custom agents and core CRM objects.

Hands-on Prototyping and Production Readiness

What to coverThe role requires a 'builder' mindset. Describe a scenario where you transitioned an AI prototype to a production-ready system. Focus on how you addressed scalability, monitoring, and technical debt during the transition.

Prompt Engineering and Orchestration Patterns

What to coverDiscuss your experience with advanced prompt engineering and orchestration patterns for complex workflows. Explain how you manage multi-step agentic tasks and ensure consistent output quality across different LLM interactions.

Monitoring AI Architecture Health

What to coverWhat specific metrics do you track to monitor the health and performance of AI-driven architectures? Explain how you differentiate between standard application monitoring and the unique needs of LLM-based systems, such as token usage, cost-efficiency, and response accuracy.

Cloud-Native Integration for AI

What to coverHow do you leverage cloud-native architecture principles to support AI workloads? Discuss your experience with containerization, microservices, or serverless patterns in the context of deploying AI agents at scale.

Behavioural practice

Bridging Strategy and Execution

What to coverDescribe a situation where you had to translate a high-level AI vision into actionable technical requirements for multiple engineering teams. How did you ensure alignment and resolve technical conflicts between the teams?

Stakeholder Management in Technical Projects

What to coverProvide an example of how you have communicated complex AI architectural decisions to non-technical stakeholders. How did you manage their expectations regarding feasibility, timelines, and business value?

Mentoring and Team Enablement

What to coverShare an experience where you mentored other architects or senior engineers on new AI-first design principles. How did you help them adapt to the shift from traditional software architecture to agentic systems?

Handling Technical Escalations

What to coverDescribe a time you had to provide technical escalation support for a critical issue in a complex AI deployment. What was your process for diagnosing the root cause and implementing a resolution while minimizing business impact?

Sharpen your knowledge

Topics to prepare

Agentic Architecture and Orchestration
Review the design patterns for multi-agent systems, focusing on how to manage state, tool-use, and inter-agent communication within an enterprise environment. This is critical for the role's focus on Agentforce and scaling AI solutions.
AI Governance and Responsible AI Frameworks
Study industry-standard frameworks for AI risk management, data privacy, and compliance. This is essential for the role's responsibility to establish governance standards and ensure production readiness for AI initiatives.

Skills to revise

Salesforce Platform Architecture
Practice mapping complex enterprise requirements to Salesforce-native capabilities, specifically focusing on multi-org topologies and integration patterns. This is a primary requirement for the role.
Hands-on AI Prototyping with LLMs
Use tools like Claude or Gemini to build a functional prototype of an automated agent workflow. This demonstrates the required 'builder' mindset and ability to move from concept to working demo.
Prompt Engineering and Tool-Use Patterns
Develop and refine prompts for complex reasoning tasks and define structured tool-use schemas for LLMs. This is directly relevant to the role's requirement for building and deploying AI agents.

Your preparation checklist

Map Salesforce Platform Expertise to Agentforce
Prepare a summary of your experience integrating AI solutions within the Salesforce ecosystem. Specifically, document how you have navigated multi-org or complex enterprise topologies and how you would apply these patterns to Agentforce-driven architectures.
Develop an AI Agent Prototype Walkthrough
Prepare a technical case study of an AI agent or automation you built hands-on. Focus on the 'builder' aspect: explain the tools used (e.g., LLMs, frameworks), how you ensured production readiness, and how you balanced architectural scalability with speed of delivery.
Define an AI Governance Framework
Draft a brief proposal for an AI governance and risk framework suitable for an enterprise environment. Include how you would monitor architecture health metrics like reliability, security, and cost-efficiency while ensuring responsible AI practices.
Prepare Strategic Communication Examples
Identify instances where you translated complex AI/ML technical concepts for non-technical stakeholders. Be ready to discuss how you facilitated architecture reviews and resolved technical dependencies across cross-functional teams.
Formulate Targeted Questions for the Interviewers
Prepare questions regarding the current maturity of their Agentforce implementations, the primary technical challenges in their current AI roadmap, and how the architecture team balances rapid prototyping with long-term platform stability.

Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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