Act as a senior, hands-on technical leader and trusted advisor to customers
Design, validate, and operationalize enterprise-grade data and AI architectures centered on Salesforce D360 and the Agentforce platform
Lead pre-sales technical design by analyzing customer needs
Shape best practices around generative AI and cross-platform integrations
Collaborate with AEs and SEs to build hands-on prototypes and demos
Develop thought leadership content focused on agent lifecycle and integration strategy
Act as a central technical knowledge resource for internal teams
Map and integrate external agents into Agentforce
Use Agentforce Studio and Agent Builder for chat and background agents
Lead prompt design, testing, and iteration
Facilitate workshops and quick enablement for customers and internal teams
Integrate Data Cloud, CRM, MuleSoft APIs, and external agent endpoints

7+ years of hands-on experience designing and delivering data, analytics, and AI architectures in enterprise environments
Hands-on experience with Salesforce Agentforce and deep fluency with D360 and core Salesforce platform services
Strong understanding of external agent ecosystems and interoperability
Experience in prompt engineering, agent lifecycle management, and hands-on prototype development
Experience with machine learning concepts (predictive and generative AI)
Deep expertise in modern cloud data platforms (Snowflake, Databricks, BigQuery, Redshift) and data ingestion patterns (batch, streaming, CDC)
Proficiency in programming (e.g., JavaScript, Python, SQL, R) and data frameworks like pandas or Jupyter
Strong presentation skills; adept at explaining complex ideas and guiding stakeholders toward impactful solutions
Passion for exploring new AI frameworks, sharing insights, and experimenting with cutting-edge technologies.
Experience integrating Salesforce with external agents via APIs and open standards (MCP, A2A)
Familiarity with prompt governance, observability, and monitoring frameworks
Background in cross-platform integrations (e.g., Hyperscaler SDKs to Salesforce Flows)
Prior exposure to conversational voice pipelines or multimodal integrations via hyperscaler services
Exposure to frameworks (TensorFlow, PyTorch), MLOps practices, and cloud AI platforms like Google Vertex AI or AWS Sagemaker
BS in Computer Science, Engineering, Data Science, or equivalent technical field.

Principal Data and AI Architect

Key skills