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ML Support Engineer
ML Support EngineerOpenkyber • IL, United States
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ML Support Engineer

ML Support Engineer

Openkyber • IL, United States
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Job Title : AI / ML Engineer Location : Minneapolis, MN(Remote) Rate : $52 / hr on W2 or $57 / hr on 1099

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.
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