AI / Machine Learning Architect

As an Agentforce Technical Architect, youll act as a trusted advisor to our customers, guiding them through the emerging AI solutions and ensuring they realize the full value of the platform. Youll combine deep technical domain expertise in AI/ML, data infrastructure, and CRM with strong presentation and solutioning skills. Working side by side with our Account Executives, Solutions Engineers, Product, and Product Marketing teams, you will:

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 contentdemo templates, whitepapers, enablement sessionsfocused on agent lifecycle, integration strategy, and technical effectivenessAct 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, love diving into new technologies, and enjoy educating others while crafting solutions that deliver real business impact, we want to talk to you!ResponsibilitiesUnderstand 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 and 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 and Lifecycle Strategy - Lead prompt design, testing, monitoring, and iteration; define agent lifecycle best practices from development through refinement.Build Hands-On Demos and Prototypes - Co-create quick prototypes (Lead Pre-Sales Workshops - Facilitate whiteboarding, deep-dive sessions, and quick enablement for customers and internal teams.

Advise on Data and Integration- Integrate Data Cloud, 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 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.RequirementsTechnical Pre-Sales/Consulting: Several years in solutions engineering, architecture, or technical consulting, ideally in B2B SaaS.Hands-on experience with Salesforce AgentforceStrong understanding of external agent ecosystems and interoperabilityProven track record in prompt engineering, agent lifecycle management, and hands-on prototype development.AI and ML Expertise: Experience with machine learning concepts (predictive and generative AI), plus the ability to communicate value to diverse audiences.CRM and Data Knowledge: Familiarity with Salesforce CRM and modern data stacks; comfortable discussing governance, security, and integration.Hands-On Development: Proficiency in programming (e.g., JavaScript, Python, SQL) or Salesforce development (Apex, Lightning Web Components, etc.).Excellent Communication: Strong presentation skills; adept at explaining complex ideas and guiding stakeholders toward impactful solutions.Curiosity and Continuous Learning: Passion for exploring new AI frameworks, sharing insights, and experimenting with cutting-edge technologies.Preferred RequirementsExperience 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.Advanced AI/ML: Exposure to frameworks (TensorFlow, PyTorch), MLOps practices, and cloud AI platforms (e.g., Google Vertex AI, AWS Sagemaker). Hands-on work with Generative AI, Large Language Models (LLMs), agent-based frameworks, and prompt ..... full job details .....