Software Engineering Mts (full Stack) - AI Generalist (demo Tools & Platform Engineering)

Salesforce · Enterprise · Hyderabad, India

Salesforce is seeking a Software Engineering MTS (Full Stack) to join their AI Generalist team, focusing on Demo Tools & Platform Engineering. The role involves designing and building internal AI-powered platforms, including full-stack applications, scalable backend services, and LLM-powered agents. Responsibilities include developing agent orchestration, tool integration, RAG systems, and working with both frontend and backend technologies to deliver end-to-end solutions for internal workflows and demo experiences.

What you'd actually do

  1. Design and build full-stack applications (frontend + backend) for internal AI-powered tools
  2. Develop and maintain scalable backend services, APIs, and microservices
  3. Build and integrate LLM-powered agents into internal workflows (automation, orchestration, task execution)
  4. Design and implement reusable “skills”/tools that agents can invoke to interact with internal systems
  5. Implement retrieval-augmented systems (RAG), embeddings, and context-aware data pipelines

Skills

Required

  • 4–6 years of professional software development experience
  • Strong fundamentals in software engineering, system design, and distributed systems
  • Proficiency in JavaScript/TypeScript and modern backend frameworks (Node.js preferred)
  • Working knowledge of Python for AI/ML integrations
  • Experience building and consuming RESTful APIs and microservices
  • Experience with modern frontend frameworks (React, Angular, Vue, or similar)
  • Hands-on experience integrating LLM APIs and building AI-assisted features
  • Understanding of agentic patterns (tool use, orchestration, prompt design, structured outputs)
  • Familiarity with data handling for AI systems (structured/unstructured data, APIs, storage)
  • Strong understanding of version control (Git) and collaborative workflows
  • Ability to take ownership from design through production in ambiguous environments

Nice to have

  • Experience with agent frameworks such as LangChain or AutoGPT
  • Experience with vector databases such as Pinecone or Weaviate
  • Experience building RAG pipelines and context-aware AI systems
  • Familiarity with Slack APIs, bots, and conversational interfaces
  • Experience working with Salesforce platforms (Sales, Service, Experience Cloud, Data Cloud, Agentforce)
  • Experience building internal developer platforms or enablement tooling
  • Awareness of AI system risks (prompt injection, data leakage, reliability issues)

What the JD emphasized

  • build internal platforms
  • AI-driven platforms
  • embedding agents, automation, and intelligence
  • LLM-powered agents
  • reusable skills/tools that agents can invoke
  • retrieval-augmented systems (RAG)
  • embeddings
  • context-aware data pipelines
  • agentic patterns (tool use, orchestration, prompt design, structured outputs)

Other signals

  • building internal platforms
  • AI-driven platforms
  • embedding agents, automation, and intelligence
  • LLM-powered agents
  • reusable skills/tools that agents can invoke
  • retrieval-augmented systems (RAG)
  • embeddings
  • context-aware data pipelines
  • agentic patterns (tool use, orchestration, prompt design, structured outputs)