Software Engineering Lmts- Ai/llm

Salesforce Salesforce · Enterprise · Bangalore, India

Lead MTS in Salesforce's Trusted Supply Chain (TSC) team, responsible for the architecture and engineering roadmap of the trusted-supply-chain platform. This role involves an AI-native rebuild of the supply-chain stack, designing and shipping agentic systems for vulnerability management, and ensuring operational excellence. The focus is on building next-generation security engineering for the agentic-AI era.

What you'd actually do

  1. Own the architecture vision and multi-year roadmap for Salesforce's Trusted Supply Chain platform — scanning, attribution, remediation, and runtime defense across public cloud and private infrastructure.
  2. Lead the AI-native rebuild of our supply-chain stack. Design and ship agentic systems that triage findings, author fixes, validate them in CI, and close the loop without human keystrokes for the well-understood cases - freeing engineers to focus on novel risk.
  3. Champion operational excellence. Feed incident learnings back into design. Build for fault isolation, graceful degradation, and observability from day one.
  4. Partner with PMs and EMs on build-vs-buy decisions and time-to-market vs. long-term sustainability tradeoffs.
  5. Coach Salesforce application teams on supply-chain and AppSec best practices; be the voice of TSC in the Salesforce Principal community.

Skills

Required

  • M.S. or Ph.D. in Computer Science or equivalent practical experience
  • 10+ years building applications or systems software at scale
  • 5+ years in public-cloud environments with container-based technologies (Docker, Kubernetes, Istio, service mesh)
  • Strong, active coding skills in at least two of Go, Java, Python, Rust, C# , with deep attention to code quality and secure-by-default patterns
  • Experience with stateful, service-oriented architectures and the design tradeoffs they impose (consistency, partitioning, recovery)
  • Experience with relational and NoSQL stores and pub/sub messaging (Kafka, RabbitMQ, SQS)
  • Demonstrated ability to define technology strategy and execute multi-year roadmaps through ambiguity
  • Track record of incremental architectural evolution, refactoring, and safely prototyping new features in production
  • Excellent written and verbal communication; proven ability to drive alignment with executives, peers, and engineers on contentious technical decisions
  • Hands-on experience building production systems with LLMs - RAG pipelines, function/tool calling, structured output, evals, guardrails
  • Working knowledge of agentic patterns: multi-agent orchestration, planner/executor loops, tool-augmented reasoning, agent memory and context management, human-in-the-loop checkpoints
  • Practical fluency with the modern AI developer stack — at least one of: Anthropic Claude / OpenAI / similar foundation-model APIs; agent frameworks (LangGraph, CrewAI, AutoGen, or equivalent); MCP (Model Context Protocol); evaluation harnesses (LangSmith, Braintrust, custom)
  • Proven daily use of AI-assisted coding tools (Claude Code, Cursor, Copilot) including authoring custom agents, slash commands, hooks, and skills — and a clear point of view on when to use them and when not to
  • A realistic posture on AI safety and reliability: prompt-injection defense, sandboxing untrusted tool calls, deterministic test harnesses, regression evals, cost/latency budgeting, and graceful degradation when the model gets it wrong

What the JD emphasized

  • AI-native rebuild
  • agentic systems
  • production systems with LLMs
  • agentic patterns
  • AI safety and reliability

Other signals

  • AI-native rebuild of supply-chain stack
  • agentic systems that triage findings, author fixes, validate them in CI
  • production systems with LLMs - RAG pipelines, function/tool calling
  • agentic patterns: multi-agent orchestration, planner/executor loops