Full-stack Engineering Manager

Samsara Samsara · Enterprise · India · Remote · Business Systems

Engineering Manager for Go-To-Market AI Engineering team, leading senior engineers to build and operate AI orchestration engines, CRM integrations, and agentic workflows. This hands-on leadership role involves technical contributions, people management, and roadmap ownership for AI-first GTM systems.

What you'd actually do

  1. Lead, hire, and develop a team of 4–7 senior engineers across the full GTM AI systems stack — setting a high bar for technical excellence, collaboration, and delivery
  2. Stay hands-on: contribute to system architecture, participate in code and design reviews, pair with engineers on complex problems, and own critical technical decisions for the team's most impactful systems
  3. Own the engineering roadmap for GTM AI systems — partnering with Sales Ops, BizTech, Finance, Legal, and GTM leadership to set priorities, define milestones, and communicate progress
  4. Drive delivery of AI-first GTM systems: agentic AI pipelines, LLM orchestration layers, Salesforce CPQ integrations, real-time Slack applications, and AWS event-driven architectures
  5. Champion engineering quality: establish and uphold standards for code review, testing, observability (distributed tracing, alerting, SLAs), incident response, and documentation

Skills

Required

  • Python
  • TypeScript/JavaScript
  • Salesforce platform experience
  • AWS expertise
  • Experience building and deploying LLM-powered systems using OpenAI API, Anthropic API, LangChain, LangGraph, or equivalent agentic frameworks
  • Demonstrated ability to lead multi-engineer technical efforts
  • Track record of effective people management
  • Strong cross-functional communication skills

Nice to have

  • Experience designing and operating low-code automation platforms (Workato, MuleSoft, Salesforce Flow) alongside custom-built agentic systems
  • Background in GTM engineering, RevOps tooling, or sales technology platform development in a high-growth B2B SaaS environment

What the JD emphasized

  • built and shipped production AI systems
  • take an agentic workflow from prototype to enterprise reliability
  • responsible AI development

Other signals

  • AI-first infrastructure
  • AI orchestration engines
  • agentic workflows
  • LLM orchestration layers
  • agentic AI pipelines