AI/ML Engineer- Healthcare- Visa Independent

Job Title: AI/ML Engineer
Location: Minneapolis, MN(Remote)

Job Description:    
1.    Design, develop, test, document, and deploy Salesforce solutions based on business needs.
2.    Develop and deploy AI/ML models for real-time decision-making and automation.
3.    Integrate AI/ML solutions into Salesforce CRM to enable intelligent data retrieval, personalized recommendations, workflow automation, forecasting, scoring, and opportunity insights.
4.    Enhance Salesforce applications with advanced AI features using both native (Einstein/Einstein GPT) and external technologies (Python-based models or Azure/AWS ML services).
5.    Build and optimize Retrieval-Augmented Generation (RAG) pipelines using vector databases and Large Language Models (LLMs) for improved contextual understanding within Salesforce workflows.
6.    Extend platform functionality using Apex (Triggers/Classes), LWC, Aura Framework, Visualforce Pages, Apex APIs and web services.

MUST HAVE SKILLS    
1.    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
2.    5-7 years of experience in Power BI development and implementation
3.    AI/ML Expertise: Building and deploying models for real-time decision-making and automation.
4.    Integration Skills: AI/ML integration with Salesforce CRM (Einstein/Einstein GPT and external technologies like Python-based models or Azure/AWS ML services).
5.    Generative AI Knowledge: Familiarity with transformers, LLMs, and Retrieval-Augmented Generation (RAG) pipelines using vector databases.
6.    Automation Development: Creating AI-powered automation solutions, including Einstein Bots and custom bots for sales/service workflows.
7.    CI/CD Proficiency: Managing deployment processes using Git.
8.    Cloud Platforms: Experience with Azure/AWS ML services and enterprise-grade integrations.
9.    Security & Compliance: Ensuring data privacy, scalability, and reliability of AI models in production.
10.    Collaboration: Ability to work with product managers, engineers, and data teams for AI-driven enhancements.
11.    Continuous Improvement: Monitoring model accuracy and implementing feedback loops for better user experience.