Description

Role Description

As an AI and Data Technical Architect, you will act as a senior, hands-on technical leader and trusted advisor to our customers. You will be responsible for designing, validating, and operationalizing enterprise-grade data and AI architectures centered on Salesforce D360 and the Agentforce platform. This role sits at the intersection of data engineering, platform architecture, and applied AI, requiring you to guide customer Chief Data Officers and Enterprise Architects through emerging AI solutions. You will work side-by-side with Account Executives and Product teams to ensure customers realize the full value of the platform by designing scalable, secure solutions that integrate D360 into complex enterprise ecosystems—including hyperscalers, data lakes, and governance frameworks.

You will:

Lead pre-sales technical design by analyzing customer needs and recommending solutions aligned with Agentforce capabilities and integration with external agent frameworks.
Shape best practices around generative AI, agent interoperability, prompt engineering, Data Cloud, and cross-platform integrations.
Collaborate with AEs and SEs to build hands-on prototypes and demos using Agentforce and integrated external agents.
Develop thought leadership content—demo templates, whitepapers, enablement sessions—focused on agent lifecycle, integration strategy, and technical effectiveness.
Act as a central technical knowledge resource, proactively addressing internal technical inquiries, facilitating deep technical enablement, and documenting best practices to empower specialist teams across the organization.

If you are naturally curious about AI and Data, love diving into new technologies, and enjoy educating others while crafting solutions that deliver real business impact, we want to talk to you!

Responsibilities

Understand Agent Interoperability - Map and integrate external agents from hyperscalers (e.g. Copilot, Gemini) into Agentforce via open standards (MCP, A2A); design how these systems collaborate.
Enable Conversational & Background Agents - Use Agentforce Studio and Agent Builder to configure chat and background agents; integrate with external channels including voice via hyperscaler APIs.
Drive Prompt Engineering & Lifecycle Strategy - Lead prompt design, testing, monitoring, and iteration; define agent lifecycle best practices from development through refinement.
Build Hands-On Demos & Prototypes - Co-create quick prototypes (<2 weeks) with AEs demonstrating integration between Agentforce and external agents or services.
Lead Pre-Sales Workshops - Facilitate whiteboarding, deep-dive sessions, and quick enablement for customers and internal teams.
Advise on Data & Integration - Integrate Data Cloud (D360), CRM, MuleSoft APIs, and external agent endpoints ensuring cohesive architectures that align with compliance and governance policies.
Support Early Adoption - Occasionally assist in proof-of-value engagements post-sale by tuning agents and guiding customers toward self-sufficient enablement.
Own Technical Architecture Decisions - Oversee data modeling, identity resolution, real-time vs. batch patterns, data graph design, and activation strategies within D360.
Own Technical Enablement - Create and manage accessible technical documentation, knowledge bases, and FAQ resources to rapidly resolve internal technical inquiries, empowering specialist teams to handle technical discussions confidently.

Requirements

Technical Pre-Sales/Consulting - 10+ years of hands-on experience designing and delivering data, analytics, and AI architectures in enterprise environments.
Salesforce Expertise - Hands-on experience with Salesforce Agentforce and deep fluency with D360 and core Salesforce platform services.
External Ecosystem Knowledge - Strong understanding of external agent ecosystems and interoperability.
Proven Track Record - Experience in prompt engineering, agent lifecycle management, and hands-on prototype development.
AI & ML Expertise - Experience with machine learning concepts (predictive and generative AI), plus the ability to communicate value to diverse audiences.
Data Stack Knowledge - Deep expertise in modern cloud data platforms (Snowflake, Databricks, BigQuery, Redshift) and data ingestion patterns (batch, streaming, CDC).
Hands-On Development - Proficiency in programming (e.g., JavaScript, Python, SQL, R) and data frameworks like pandas or Jupyter.
Excellent Communication - Strong presentation skills; adept at explaining complex ideas and guiding stakeholders toward impactful solutions.
Curiosity & Continuous Learning - Passion for exploring new AI frameworks, sharing insights, and experimenting with cutting-edge technologies.

Preferred Requirements

Advanced Integration - Experience integrating Salesforce with external agents via APIs and open standards (MCP, A2A).
Governance & Observability - Familiarity with prompt governance, observability, and monitoring frameworks.
Cross-Platform Background - Background in cross-platform integrations (e.g., Hyperscaler SDKs to Salesforce Flows).
Multimodal Pipelines - Prior exposure to conversational voice pipelines or multimodal integrations via hyperscaler services.
Advanced AI/ML Frameworks - Exposure to frameworks (TensorFlow, PyTorch), MLOps practices, and cloud AI platforms like Google Vertex AI or AWS Sagemaker.
Education - BS in Computer Science, Engineering, Data Science, or equivalent technical field.