AI​/ML Engineer Salesforce Automation

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.