Director, Engineering - Agentic Search & AI Components

What You’ll Do

• Architect Search & Retrieval Systems:
  Design and implement robust search indices that enable AI agents to perform complex retrievals across the Salesforce data ecosystem.
• Lead AI Component Development:
  Oversee the creation of the semantic layer and embedding pipelines necessary for grounding Agentic AI in Customer Success data.
• Team Leadership:
  Lead, mentor, and manage a high‑performing team of data and AI engineers, fostering technical excellence and career growth.
• Strategic
  
  Roadmap:
  
  In partnership with product managers, define the technical vision for agentic retrieval, aligning search strategy with the broader migration to Data Cloud and the evolution of AI‑driven personalized engagements.
• Operational Excellence:
  Establish rigorous standards for data quality, latency, and index freshness to ensure agents provide reliable, real‑time insights.
• Cross‑Functional
  
  Collaboration:
  
  Partner with Data Scientists, Product Managers, and the other engineering leaders to translate complex business needs into scalable technical solutions.
• AI Integration & Automation:
  Lead efforts to automate the data delivery pipeline, ensuring seamless integration between internal databases, third‑party APIs, and the AI orchestration layer.

• A related technical degree required.
• Professional
  
  Experience:
  
  10+ years in engineering, with a significant focus on search technology, vector databases, data engineering, and AI/ML infrastructure.
• Technical Proficiency:
  Deep expertise in Search Indices (e.g., Pinecone, Milvus, Redis);
  
  Experience with Workflow Orchestration tools like Airflow or dbt;
  Strong programming skills in Python, Java, or Scala, and experience with data frameworks like Spark and Pandas/Polars;
  Hands‑on experience with Cloud Platforms (AWS, Azure) and modern data warehousing (Big Query, Redshift, Data Cloud);
  Hands‑on experience with the Salesforce ecosystem (Data
  360, Agentforce, Service Cloud, etc.).
• AI & Data Science Domain Knowledge:
  Understanding of embedding models, LLM grounding techniques, and semantic layer construction.
• Soft Skills:
  
  Exceptional ability to communicate complex technical concepts (like vector similarity or RAG architecture) to non‑technical stakeholders.
• Leadership Track Record:
  Proven experience managing engineering teams in a fast‑paced, enterprise environment.

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