Principal Software Engineer, AI

Salesloft Salesloft · Enterprise · Atlanta, GA · Engineering

Principal Software Engineer, AI to lead the design and build-out of the AI Context Layer, a data platform that gives AI agents access to high-quality, well-governed context. This includes a knowledge graph, RAG engines, retrieval/search infrastructure, and the associated access control and governance systems. The role requires deep expertise in RAG architectures, vector databases, and retrieval systems, as well as experience designing developer-facing APIs and SDKs, and understanding data governance requirements.

What you'd actually do

  1. Embed with feature teams across the organization to understand what they need from the context layer - what data, at what quality, in what form - and use those insights to drive the platform roadmap.
  2. Build the technical vision and roadmap for our Context Layer: a collection of high-quality data products - including a knowledge graph, RAG engines, and retrieval/search infrastructure - that give AI agents access to the best possible context about our customers and their revenue processes.
  3. Design and own the access control and RBAC model for the context layer - a genuinely hard problem at the intersection of multi-tenant data, agent identity, and fine-grained permissions.
  4. Define and evolve the APIs, SDKs, and developer interfaces that teams use to interact with the context layer, ensuring they are ergonomic, well-documented, and built for scale.
  5. Identify data platform dependencies and work closely with data engineering to ensure the underlying data infrastructure can reliably service the context layer's needs.

Skills

Required

  • 12+ years of professional software engineering experience, with a strong focus on data-layer or search/retrieval infrastructure
  • Proven experience designing and building knowledge graphs and/or large-scale retrieval and search systems in production
  • Deep expertise in RAG architectures, vector databases, and embedding-based retrieval - including evaluation, quality tuning, and relevance optimization
  • Strong understanding of access control and RBAC design in multi-tenant, data-rich environments, with the judgment to navigate the tradeoffs involved
  • Experience designing developer-facing APIs and SDKs, with a track record of building interfaces that are intuitive and well-adopted internally
  • Familiarity with data governance, lineage, and audit-ability requirements - particularly in enterprise or regulated contexts
  • Demonstrated ability to lead technical direction across multiple teams and drive complex, multi-stakeholder p

What the JD emphasized

  • AI agents
  • knowledge graph
  • RAG engines
  • retrieval/search infrastructure
  • access control and RBAC model
  • developer-facing APIs and SDKs
  • data governance, lineage, and auditability

Other signals

  • AI agents
  • knowledge graph
  • RAG engines
  • retrieval/search infrastructure
  • access control and RBAC model
  • developer-facing APIs and SDKs
  • data governance, lineage, and auditability