Inherent Technologies
Position: AI/ML Engineer
Location: Minneapolis, MN/Remote
Duration: 1 Years
Job Description Design, develop, test, document, and deploy Salesforce solutions based on business needs. Develop and deploy AI/ML models for real-time decision-making and automation. Integrate AI/ML solutions into Salesforce CRM to enable intelligent data retrieval, personalized recommendations, workflow automation, forecasting, scoring, and opportunity insights. Enhance Salesforce applications with advanced AI features using both native (Einstein/Einstein GPT) and external technologies (Python-based models or Azure/AWS ML services). Build and optimize Retrieval-Augmented Generation (RAG) pipelines using vector databases and Large Language Models (LLMs) for improved contextual understanding within Salesforce workflows. Extend platform functionality using Apex (Triggers/Classes), LWC, Aura Framework, Visualforce Pages, Apex APIs and web services. Must Have Skills Bachelor’s degree in Computer Science, Information Systems, Statistics, or a comparable discipline is required, with prior experience in data analysis or a related field being advantageous 5-7 years of experience in Power BI development and implementation AI/ML Expertise: Building and deploying models for real-time decision-making and automation. Integration Skills: AI/ML integration with Salesforce CRM (Einstein/Einstein GPT and external technologies like Python-based models or Azure/AWS ML services). Generative AI Knowledge: Familiarity with transformers, LLMs, and Retrieval-Augmented Generation (RAG) pipelines using vector databases. Automation Development: Creating AI-powered automation solutions, including Einstein Bots and custom bots for sales/service workflows. CI/CD Proficiency: Managing deployment processes using Git. Cloud Platforms: Experience with Azure/AWS ML services and enterprise-grade integrations. Security & Compliance: Ensuring data privacy, scalability, and reliability of AI models in production. Collaboration: Ability to work with product managers, engineers, and data teams for AI-driven enhancements. Continuous Improvement: Monitoring model accuracy and implementing feedback loops for better user experience.
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Location: Minneapolis, MN/Remote
Duration: 1 Years
Job Description Design, develop, test, document, and deploy Salesforce solutions based on business needs. Develop and deploy AI/ML models for real-time decision-making and automation. Integrate AI/ML solutions into Salesforce CRM to enable intelligent data retrieval, personalized recommendations, workflow automation, forecasting, scoring, and opportunity insights. Enhance Salesforce applications with advanced AI features using both native (Einstein/Einstein GPT) and external technologies (Python-based models or Azure/AWS ML services). Build and optimize Retrieval-Augmented Generation (RAG) pipelines using vector databases and Large Language Models (LLMs) for improved contextual understanding within Salesforce workflows. Extend platform functionality using Apex (Triggers/Classes), LWC, Aura Framework, Visualforce Pages, Apex APIs and web services. Must Have Skills Bachelor’s degree in Computer Science, Information Systems, Statistics, or a comparable discipline is required, with prior experience in data analysis or a related field being advantageous 5-7 years of experience in Power BI development and implementation AI/ML Expertise: Building and deploying models for real-time decision-making and automation. Integration Skills: AI/ML integration with Salesforce CRM (Einstein/Einstein GPT and external technologies like Python-based models or Azure/AWS ML services). Generative AI Knowledge: Familiarity with transformers, LLMs, and Retrieval-Augmented Generation (RAG) pipelines using vector databases. Automation Development: Creating AI-powered automation solutions, including Einstein Bots and custom bots for sales/service workflows. CI/CD Proficiency: Managing deployment processes using Git. Cloud Platforms: Experience with Azure/AWS ML services and enterprise-grade integrations. Security & Compliance: Ensuring data privacy, scalability, and reliability of AI models in production. Collaboration: Ability to work with product managers, engineers, and data teams for AI-driven enhancements. Continuous Improvement: Monitoring model accuracy and implementing feedback loops for better user experience.
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