Smts/lmts - AI ML Engineers

Salesforce Salesforce · Enterprise · Bangalore, India

Salesforce is seeking an AI/ML Engineer to join their Agentic AI team, focusing on Data Management and building scalable AI solutions like Agents, AI Copilots, Chatbots, AI Planners, and RAG solutions. The role involves technical leadership, architectural guidance, and driving the full SDLC for Agentic AI products within an enterprise context.

What you'd actually do

  1. Design, implement, test and deliver highly scalable AI solutions: Agents, AI Copilots/assistants, Chatbots, AI Planners, RAG solutions.
  2. Lead the design, development, and quality strategy for scalable data services that empower enterprises.
  3. Provide architectural guidance and mentorship to a scrum team while leading code reviews and design sessions.
  4. Make critical design decisions based on performance, scalability, and reliability, including high-level (HLD) and low-level design (LLD).
  5. Drive technical roadmaps for Agentic AI products and own the entire SDLC.

Skills

Required

  • 6-15 years of experience in building highly scalable Software-as-a-Service applications/platform.
  • Experience building technical architectures that deliver Agentic Solutions to address end to end Customer use cases
  • Deep expertise in Model Architectures and AI Systems, including orchestration for large-scale production deployments.
  • Advanced Context Engineering, demonstrated through designing and building Agentic Experience and RAG solutions/Alternates for high-quality, contextualized AI experiences.
  • Proficiency in distributed systems, event-driven architecture and cloud native technologies like Kubernetes
  • Strong Evaluation-led benchmarking (NFRs, SLIs, SLOs) to ensure performance, scalability, and Customer Trust in AI/ML systems.
  • Technical Leadership in Agentic Development, including designing and implementing agentic solutions and AI interfaces.
  • Strong system design skills for Distributed AI systems at Scale
  • Demonstrated track record of cultivating strong working relationships and driving collaboration across multiple technical and business teams to resolve critical issues.
  • Experience with the full software lifecycle in highly agile and ambiguous environments.
  • Excellent interpersonal and communication skills.
  • Strong proficiency in Java (Multithreading, and JVM tuning), or Python, and Object-Oriented designing.
  • Expertise in defining, packaging, and deploying structured 'Skills' (prompts/tools) for deterministic, headless agent workflows.
  • Experience with conversation-level testing, dynamic simulation environments (e.g., Benchforce/eVerse), and custom scorers for continuous agent improvement and evaluation (ADLC).
  • Ability to design and implement inter-agent protocols (A2A) and Human-Agent Collaboration (HAC) frameworks to manage shared context and ensure responsible autonomy.
  • Deep understanding and implementation of an AI Trust Layer to ground AI systems in enterprise context, ensuring security and auto-blocking threats like prompt injection.

Nice to have

  • Expertise in Data architectures including designing and implementing data pipelines and real-time data systems for high-scale AI products.
  • Familiarity with PySpark for large-scale data processing

What the JD emphasized

  • building highly scalable Software-as-a-Service applications/platform
  • building technical architectures that deliver Agentic Solutions
  • Deep expertise in Model Architectures and AI Systems, including orchestration for large-scale production deployments
  • Advanced Context Engineering, demonstrated through designing and building Agentic Experience and RAG solutions/Alternates for high-quality, contextualized AI experiences
  • Strong Evaluation-led benchmarking (NFRs, SLIs, SLOs) to ensure performance, scalability, and Customer Trust in AI/ML systems
  • Technical Leadership in Agentic Development, including designing and implementing agentic solutions and AI interfaces
  • Strong system design skills for Distributed AI systems at Scale
  • Expertise in defining, packaging, and deploying structured 'Skills' (prompts/tools) for deterministic, headless agent workflows
  • Experience with conversation-level testing, dynamic simulation environments (e.g., Benchforce/eVerse), and custom scorers for continuous agent improvement and evaluation (ADLC)
  • Ability to design and implement inter-agent protocols (A2A) and Human-Agent Collaboration (HAC) frameworks to manage shared context and ensure responsible autonomy
  • Deep understanding and implementation of an AI Trust Layer to ground AI systems in enterprise context, ensuring security and auto-blocking threats like prompt injection

Other signals

  • AI CRM
  • Agentic AI
  • CLAIRE Agentic Data Management
  • AI Copilots/assistants
  • RAG solutions
  • Agentic Data Management