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.