Software Engineering Pmts

Salesforce Salesforce · Enterprise · Bangalore, India, India

Senior Engineering contributor on the Agentforce platform team, owning the architecture, design, and execution of their Agentic AI platform and applications. Collaborates with software engineers, data scientists, and product managers to build scalable, production-ready systems. Focuses on backend systems, data pipelines for training and inference, and driving ML best practices.

What you'd actually do

  1. Lead architecture and development of AI-powered backend systems and services.
  2. Design and optimize data pipelines for training and inference at scale.
  3. Work with product and business teams to translate user needs into technical requirements.
  4. Set technical direction and mentor engineers across teams.
  5. Drive adoption of ML best practices for model training, deployment, monitoring, and governance.

Skills

Required

  • 12+ years of software engineering experience
  • 3+ years building AI/ML systems at scale
  • object oriented programming
  • Java
  • Applied AI and AI applications
  • LLMs
  • vector databases
  • applied generative AI
  • system design
  • distributed systems
  • cloud-native architectures (AWS/GCP)
  • Agentic AI experiences
  • ML pipelines
  • data engineering workflows
  • API platforms
  • lead cross-functional teams
  • mentor engineers
  • communication and collaboration skills
  • translate complex AI concepts into pragmatic engineering decisions
  • startups or high-growth tech companies

Nice to have

  • API development
  • API lifecycle management
  • client SDKs development
  • AWS sagemaker
  • terraform
  • spinnaker
  • EKS
  • GKE
  • data engineering
  • data pipelines
  • distributed systems
  • Agile development methodologies
  • pair programming
  • continuous integration (CI)
  • continuous deployment (CD)
  • Salesforce Core technology
  • production customer escalations
  • debugging and problem solving skills
  • cultivating strong working relationships
  • driving collaboration

What the JD emphasized

  • building AI/ML systems at scale
  • building and scaling Agentic AI experiences
  • building and scaling ML pipelines
  • building and scaling data engineering workflows
  • building and scaling API platforms

Other signals

  • Agentic AI platform
  • production-ready systems
  • ML pipelines
  • API platforms