Business Systems Automation Engineer
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