Senior Ai/ml Engineer - Smts

Salesforce Salesforce · Enterprise · Redwood City, CA

Senior AI/ML Engineer at Salesforce focusing on the Agentic AI team, specifically for Data Management. The role involves developing, implementing, and delivering scalable Agentic AI services like Agents, Copilots, Chatbots, AI Planners, and RAG solutions. It requires leading design and quality strategies for data services, analyzing technical feasibility, driving long-term design strategies, and collaborating with cross-functional teams. The position also involves troubleshooting production issues and acting as a domain expert.

What you'd actually do

  1. Develop, implement, test, and deliver highly scalable Agentic AI services, including Agents, AI Copilots/assistants, Chatbots, AI Planners, and RAG solutions.
  2. Lead the design, development, and quality strategy for scalable data services that empower enterprises.
  3. Analyze and provide feedback on technical feasibility.
  4. Drive long-term design strategies that span multiple sophisticated projects, deliver technical reports and performance presentations to customers and at industry events.
  5. Actively communicate with, encourage and motivate all levels of staff. Drive technical knowledge sharing across cross-functional teams

Skills

Required

  • 5+ years of experience in building highly scalable Software-as-a-Service applications/platform.
  • Experience building technical architectures that deliver Agentic Solutions and AI Interfaces at Scale to address end to end Customer use cases
  • Thrive in dynamic environments, working on cutting edge projects that often come with ambiguity.
  • Innovation/startup mindset to be able to adapt.
  • Deep and foundational expertise in Model Architectures and AI Systems, including orchestration for large-scale production deployments.
  • Proficiency in distributed systems, cloud native technologies like Kubernetes, multithreading, concurrency, caching, queues, and event-driven architecture
  • 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.
  • 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 coding proficiency in Java (Multithreading, and JVM tuning), or Python, and Object-Oriented design, focused on building highly scalable Agentic AI services.
  • 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 and distributed computing tasks within AI pipelines.
  • Experience with continuous integration (CI) and continuous deployment (CD), and service ownership.

What the JD emphasized

  • building highly scalable Software-as-a-Service applications/platform
  • building technical architectures that deliver Agentic Solutions and AI Interfaces at Scale
  • Deep and foundational 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.
  • 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.
  • 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

  • Agentic AI services
  • CLAIRE Agentic Data Management
  • AI Copilots/assistants
  • Chatbots
  • AI Planners
  • RAG solutions
  • AI Interfaces at Scale
  • Agentic Solutions
  • Context Engineering
  • AI Trust Layer