Project Role : Packaged/SaaS Application Engineer
Project Role Description : Configure and support packaged or SaaS applications to adapt features, manage releases, and ensure system stability. Use standard tools, APIs, and low-code platforms to align solutions with business needs while preserving compatibility and performance.
Must have skills : Agentforce
Good to have skills : NA
Minimum 12 year(s) of experience is required
Educational Qualification : 15 years full time education
Summary:
AI Powered Tech Talent. As a Salesforce AI Core Architect, you will own the technical architecture of AI core platforms and services across multiple workstreams or mid-sized programs. You will drive the adoption of Generative AI, RAG, and agentic systems across Salesforce and other enterprise channels, ensuring solutions are scalable, secure, and aligned with enterprise architecture standards.
The role emphasizes hands-on architecture, technical leadership, and institutionalizing AI-accelerated engineering practices across teams.
Roles & Responsibilities:
Own end-to-end technical architecture for AI core capabilities consumed by Salesforce and other enterprise applications.
Define and govern architecture for AI services, orchestration layers, guardrails, and evaluation pipelines across multiple workstreams.
Design and oversee implementation of RAG architectures, semantic search solutions, and vector-based knowledge stores integrated with Salesforce and external systems.
Architect multi-cloud AI integrations leveraging Azure OpenAI, AWS Bedrock/SageMaker, and Google Cloud AI where appropriate.
Lead technical teams, conduct design reviews and code reviews, and ensure compliance with architectural principles and security standards.
Integrate Generative AI and agentic workflows into Salesforce processes (e.g., sales, service, marketing, industries) through Apex, LWCs, Flows, and platform events.
Collaborate with enterprise architects, security, and compliance teams to align AI core initiatives with organizational policies and Responsible AI guidelines.
Define non-functional requirements (NFRs) and ensure AI solutions meet performance, scalability, reliability, and observability benchmarks.
Mentor senior developers and associate architects, building high-performing teams and promoting AI-assisted engineering practices.
Evaluate and recommend emerging AI frameworks, tools, and patterns, and drive the roadmap for AI core capabilities.
Contribute to or lead architecture workshops, client technical discussions, and solution reviews.
Professional & Technical Skills:
Must Have Skills
Strong expertise in Salesforce Technical Architecture with experience across Sales, Service, Experience, and at least one Industry Cloud.
Deep hands-on experience with:
o Apex
o Triggers
o Lightning Web Components (LWC)
o Salesforce Flows
o Integrations (REST, SOAP, event-driven integrations)
Strong understanding of Generative AI, LLMs, RAG, and agentic system design.
Experience architecting and delivering AI solutions using at least one major cloud AI platform (Azure, AWS, or Google).
Solid grounding in cloud computing and enterprise architecture principles.
Experience with DevOps and CI/CD for Salesforce and microservices-based AI workloads.
Proven ability to lead technical teams and manage multiple concurrent workstreams.
Good to Have Skills
Experience building internal AI platforms (prompt stores, evaluation and feedback loops, model routing, governance dashboards).
Exposure to data engineering patterns relevant for AI consumption (feature stores, embeddings pipelines, streaming).
Certifications Required
Salesforce System Architect or Application Architect – Mandatory
Salesforce Platform Developer II – Mandatory
Salesforce AI Associate – Mandatory
Salesforce Agentforce Specialist – Mandatory
GenAI hands-on project experience – Desirable
At least one advanced AI/Cloud certification (e.g., Azure AI Engineer, AWS Machine Learning Specialty, Google Professional ML Engineer) – Preferred
Additional Information:
10–12+ years of experience in Salesforce Technical Architecture.
Experience managing multiple technical workstreams.
Exposure to enterprise-scale delivery models.
15 years of full-time education is required.
15 years full time education
