We're building the next layer of intelligent CRM - where language models, automated workflows, and structured business data work together to drive real outcomes. As an AI Automation & CRM Integration Engineer, you'll sit at the intersection of AI development and Salesforce-powered operations, designing and deploying systems that turn natural language into business action. This isn't a role for someone who wants to stay in a sandbox. You'll be connecting live LLM pipelines to production CRM systems, engineering the reasoning layers that make those connections useful, and building automation flows that scale across the business
Key Accountabilities:- Design and deploy multi-step AI automation flows that connect LLM inference, CRM triggers, webhook events, and data transforms across integrated systems
- Build and maintain full-stack integrations between large language model APIs and Salesforce - reading and writing CRM context dynamically into model prompts and back into SFDC objects and flows
- Engineer prompt templates, few-shot frameworks, and output validation logic to ensure AI responses meet business-defined quality standards
- Develop and run LLM evaluation pipelines using frameworks such as RAGAS, TruLens, or equivalent, measuring model performance against real business outcomes
- Collaborate with Tableau and Salesforce stakeholders to surface AI-driven insights through dashboards and reporting workflows
- Translate technical model outputs into interpretable business signals, supporting revenue operations and pipeline visibility
Desired Skills and Experience: Required: - Multi-step AI automation Builds end-to-end AI workflows across connected systems - triggering model calls, routing outputs, and updating downstream platforms without manual handoffs.
- Full-stack LLM + Salesforce integration Connects LLM APIs directly to Salesforce objects and flows - designing pipelines that pull live CRM context into model prompts and write structured outputs back into SFDC.
- Prompt engineering & LLM evaluation Engineers prompts systematically and measures their performance - from few-shot design and output validation to RAG retrieval quality and business-aligned evaluation frameworks.
- Knowledge-based AI & ontology design Understands how to structure what a system needs to know - defining entity types, relationships, and semantic hierarchies so that AI pipelines reason accurately over real-world information.
Preferred: - Exposure to revenue operations workflows, pipeline reporting, or CRM-driven business processes
- Experience working with Tableau or SFDC dashboards in a cross-functional analytics context
- Ability to frame AI outputs in terms of business value - lead conversion, churn reduction, process efficiency
Tech stack signals we look for Python • SQL • LLM APIs (OpenAI / Anthropic / HuggingFace) • Salesforce (RESTAPI / Flows / Objects) • RAG frameworks (LangChain, LlamaIndex) • Vector databases (FAISS, Pinecone, Weaviate) • Ontology / semantic modeling (RDF, OWL, SKOS or equivalent) • Workflow orchestration platforms (N8N, Airflow, or equivalent) • MLOps / managed AI infrastructure (Shakudo, SageMaker, or equivalent) • Tableau • ETL / data pipeline tooling • Git
You might be a fit if you've - Built a chatbot or assistant that reads from and writes to a live data system
- Designed a RAG pipeline where the retrieval layer was structured around real business entities (accounts, contacts, opportunities)
- Written evaluation logic that tests model output quality - not just accuracy, but relevance and business alignment
- Modeled an information flow - even in a project setting - where entity relationships shaped how a system reasoned or retrieved
We value demonstrated ability over credentials. If your work shows the thinking, we want to hear from you.
#LI-Hybrid
Base Salary Range
$100,000-$125,000 USD
