AI/ML Engineer- Healthcare- Visa Independent

Qualifications

• 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

Responsibilities

• 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

Full Description

Job Title: AI/ML Engineer

Location: Minneapolis, MN(Remote)

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.