Manager, AI Engineering

Salesforce Salesforce · Enterprise · Mexico City, Mexico

Manager of AI Engineering at Salesforce, leading a team focused on building the agentic testing framework for the Agentforce Supply Chain product. The role involves people management, defining technical roadmaps, ensuring operational excellence including safety guardrails, and cross-functional leadership. Requires strong software architecture and distributed systems background, with experience in scaling AI/ML products and understanding the LLM lifecycle.

What you'd actually do

  1. Hire, coach, and inspire a world-class team of AI and distributed systems engineers. You will be responsible for the career growth and performance of this team.
  2. Define and drive the multi-year technical roadmap for your area, ensuring your team’s innovation aligns with the broader Agentforce mission while setting clear, measurable goals for engineering success.
  3. You will uphold the processes for deployment, reliability, and safety guardrails that allow our agents to operate in mission-critical environments.
  4. Act as the primary technical bridge between Product Management, Design, and Salesforce Executive Leadership to drive feature delivery and business impact.

Skills

Required

  • 5+ years of professional engineering experience
  • 2-4+ years of direct people management
  • leading high-performing teams in a fast-paced environment
  • software architecture
  • distributed systems
  • leading deep-dive architectural reviews for AI-driven backends
  • unblock team members
  • quickly prototype solutions
  • proactively do chores that reinforce a “see something fix it!” team culture
  • Exceptional ability to communicate complex technical roadmaps to non-technical executive stakeholders and customers

Nice to have

  • Experience leading multiple complex workstreams simultaneously
  • Understanding of the Salesforce ecosystem, Data Cloud, or Agentforce capabilities
  • nuances of the LLM lifecycle, from prompt orchestration to production serving (vLLM/SGLang)

What the JD emphasized

  • agentic testing framework
  • AI-powered platform
  • agentic era
  • safety guardrails
  • scaling AI/ML products
  • prompt orchestration
  • production serving

Other signals

  • AI-powered platform for designing, automating, and running end-to-end business processes
  • leading the team that is building the agentic testing framework
  • uphold the processes for deployment, reliability, and safety guardrails that allow our agents to operate in mission-critical environments
  • Proven experience scaling AI/ML products
  • nuances of the LLM lifecycle, from prompt orchestration to production serving