Head of Engineering - AI - Data Resilience​/Salesforce

My client is looking for a Head of Engineering & AI Products who will will lead the development of the unified engineering platform and shared infrastructure underpinning critical, customer-facing capabilities across the group's Salesforce-ecosystem products.

Location:
Hybrid - Boston or NYC area.

• Designing, staffing, and leading the unified product organization, including the leveling and career paths that make a mergedteam credible to both product and engineering people.
• The delivery cadence, quality gates, and on-call and reliability practice that back our enterprise commitments — velocity gainedby removing layers, not by removing rigor.
• SOC 2 posture as a product and engineering responsibility built into the pipeline rather than bolted on before the audit.
• Partnership with Marketing and Sales on positioning, launch, enablement, analyst relations, and partner motions.

• Growing the AI products from the Intelligent Context Layer and into the primary reason customers choose the solution.
• Retrieval architecture as a product decision.
• The AI cost and latency envelope. Inference cost per answer and query cost against the customer's lake, tracked as hard productconstraints as usage grows.

• The multi-source connector roadmap and its build order, defended with evidence
• Scope discipline per connector. Each new source ships to a fixed minimum-viable surface
• Persona and application expansion.

In addition, The platform is designed to help customers protect their Salesforce (and others) data, manage it through its full lifecycle

• Salesforce & other Data Replication
• Backup and Restore
  .
• Data Archive / Time Machine
• Data Lake /Data Lakehouse
• API Integration to Snowflake, Databricks, Veeva and others.
• Observability

• 10+ years in product management for enterprise B2B SaaS, including at least 4 years leading product teams and managing product managers.
• Working technical fluency in retrieval-augmented generation, context engineering, embeddings, and vector databases.
• Demonstrated experience building AI capability into a product that shipped and was used in production
• Hands-on use of AI tooling inside the development process.
• Experience leading a combined product and engineering team.
• A proven, verifiable track record: products you took to market, the segments you won, and the revenue you produced.
• Experience with data platforms, data infrastructure, integration, or analytics products sold to enterprises.
• Experience selling into and supporting large, regulated enterprises with procurement, security review, and compliance requirements.

• Hands0n familiarity with specific vector and hybrid retrieval stacks (pgvector,
• Open Search or Elasticsearch, Pinecone, Weaviate, LanceDB, or equivalent)
• Products involving natural-language-to-query generation, semantic layers, or metadata-driven query construction over a warehouse or lake.
• Entity resolution, identity graphs, or record-linkage products
• Fluency with open table formats and cloud data platforms:
  Iceberg, Delta Lake, Parquet, Athena, Snowflake, Databricks, Redshift, Big Query.
• Product experience shaped by compliance requirements — SOC 2, GDPR, HIPAA, DORA, data residency, and long-horizon or immutable retention.
• Model Context Protocol, agent tool design, or comparable agentic integration surfaces.
• Commercial fluency with enterprise and consumption-based pricing models
• Taking a company from a single product to a multi-product portfolio without fragmenting the customer experience.

PLEASE NOTE: ONLY USA NATIONAL OR GREEN CARD HOLDERS WILL BE CONSIDERED FOR THIS ROLE.

AUTOMATIC DISQUALIFICATION FOR ALL NON-RESIDENTS.