Lead Engineer Salesforce AI Agentforce  

Define, document, and enforce Emerson AI governance policies, ethical guidelines, compliance expectations, and approval standards for Salesforce AI capabilities, including Agentforce and Einstein.

Review proposed AI toolsets and agent use cases across business functions for regulatory, ethical, privacy, security, and business-process readiness.

Establish practical guardrails for agent design, topic configuration, actions, data grounding, escalation, auditability, and human oversight.

Track evolving AI regulations, Salesforce trust guidance, and corporate policies, translating requirements into usable governance practices for delivery teams.

Create AI risk assessment templates, model/use-case review checkpoints, exception processes, and remediation procedures for Salesforce AI deployments.

Advise workstream teams on Agentforce design patterns, agent topics, actions, instructions, prompt quality, hallucination prevention, and safe handoff to human users.

Enable responsible AI innovation by helping teams move from concept to governed pilots, production readiness, and measurable business outcomes.

Partner with Data Cloud, Data Stewardship, and Integration teams to ensure agent grounding data is accurate, permission-aware, compliant, and fit for intended use.

Develop reusable Agentforce patterns, documentation, training materials, and playbooks to accelerate consistent adoption across Business Groups and World Areas.

Audit AI agent usage, performance, quality, compliance, bias risk, and outcome measures, coordinating remediation actions when issues are identified.

Define AI trust and safety metrics, adoption metrics, value measures, and executive reporting for Salesforce AI capabilities.

Partner with Security, Legal, Compliance, Architecture, and Data teams to ensure AI controls align with Emerson enterprise policies and risk posture.

Support post-deployment reviews, incident learnings, and continuous improvement of AI governance standards and agent operating models.

Knowledge of Salesforce Agentforce concepts, including agent design, topics, actions, instructions, guardrails, grounding, human handoff, and monitoring.

Knowledge of Salesforce Einstein and AI-enabled CRM capabilities, with understanding of Salesforce Data Cloud as an AI data foundation.

Experience with AI/ML governance frameworks, responsible AI principles, model/use-case risk assessment, bias monitoring, and AI audit practices.

Knowledge of data privacy regulations and compliance considerations, including GDPR, CCPA, industry-specific requirements, and enterprise security controls.

Understanding of LLM capabilities, limitations, hallucination risk, prompt engineering concepts, retrieval/grounding patterns, and safe automation design.

Experience influencing and educating cross-functional stakeholders across business, delivery, data, security, legal, compliance, and architecture teams.

Experience with risk assessment and mitigation planning, with the judgment to distinguish acceptable experimentation from unacceptable enterprise risk.

Ability to clearly communicate AI concepts, controls, and trade-offs to non-technical leaders and delivery teams.

Experience applying a pragmatic governance approach that accelerates responsible innovation rather than creating unnecessary friction.

Curiosity, regulatory awareness, and disciplined learning in a rapidly evolving AI technology landscape.

Salesforce Agentforce certification.

Certification in Salesforce AI Associate, Salesforce Administrator, Data Cloud, or responsible AI/AI ethics.