NTT DATA is seeking a skilled Salesforce Architect to join our dynamic team. This role focuses on designing and implementing advanced solutions across Agentforce, Salesforce CPQ, and Revenue Cloud.

We are looking for individuals who are innovative, passionate, and eager to contribute to a forward-thinking organization. If you thrive in an inclusive and adaptable environment, this is an excellent opportunity to grow with us.

Lead the end-to-end solution architecture for Agentforce, Salesforce CPQ, and Revenue Cloud, encompassing agent-assisted and agentic workflows.

Design agent orchestration patterns, including state management, memory, and multi-agent coordination.

Architect integrations with Salesforce automation tools, Apex, Flow, APIs, MuleSoft, and external enterprise services.

Define LLM usage, deterministic logic, and workflow engines, ensuring appropriate application of generative AI.

Develop context engineering and retrieval strategies for optimal grounding and relevance.

Establish secure agent action patterns with guardrails, validation, and human-in-the-loop mechanisms.

Define AI evaluation frameworks covering performance, grounding, and business outcomes.

Ensure observability across agent reasoning, actions, and operational performance.

Assess model and platform trade-offs related to quality, latency, cost, and enterprise constraints.

Architect resilient action layers and transactional integration patterns for critical revenue processes.

Advise on consumption-based economics and AI cost optimization strategies.

Lead architecture reviews and provide technical governance to global delivery teams.

Mentor technical architects, developers, and Agentforce specialists.

A strong foundation in Salesforce Agentforce and associated agentic architecture concepts is essential.

Expertise in Salesforce CPQ, Revenue Cloud Advanced, and Revenue Lifecycle Management is required.

Deep understanding of Salesforce Sales Cloud and core platform architecture is necessary.

Proficiency in agent orchestration, tool/function calling, and multi-agent architectures is crucial.

Experience with agent state, memory management, and context management is vital.

Knowledge of RAG, semantic search, embeddings, and retrieval optimization is needed.

Familiarity with context engineering beyond basic prompt engineering is important.

Experience with evaluation frameworks and AI observability is expected.

Understanding of AI safety, grounding, guardrails, and least-privilege action design is required.

Familiarity with LLM model selection and architectural trade-offs is necessary.

Solid grasp of classic NLP concepts including intent classification and entity extraction is beneficial.

Proficiency in Apex, Lightning Platform, Flow, APIs, and enterprise integration patterns is a must.

Strong understanding of asynchronous processing, transaction boundaries, resiliency, and idempotency is key.

Expertise in security architecture, including sharing, CRUD/FLS, permission models, and data protection is required.