AI​/ML Engineer

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.

• 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.