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. 

 

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