About the role
Responsibilities:
- Design, develop, and deploy LLM-based applications for AI-driven CRM solutions on Salesforce.
- Implement Retrieval-Augmented Generation (RAG) architectures to optimize the contextual accuracy of LLMs within the Salesforce environment.
- Leverage Vector Databases (e. g., Pinecone, FAISS, or Weaviate) to manage and retrieve high-dimensional data for AI applications.
- Integrate external knowledge sources into AI models, enhancing their ability to deliver contextual and business-relevant insights.
- Collaborate closely with cross-functional teams (including RevOps, data, and engineering) to ensure AI solutions align with business objectives and scale effectively.
- Stay up-to-date with advancements in AI, including LLMs, RAG architectures, and innovations in database and cloud technology.
Requirements:
- Bachelor's or Master's degree in Computer Science, Data Science, AI/ML, or a related field.
- Proven experience working with LLMs (e. g., GPT, BERT) and implementing them in production-level applications.
- Expertise in RAG architecture for improving model relevance and performance in AI-driven systems.
- Strong experience with Vector Databases to handle large-scale, high-dimensional data.
- Proficiency in Python and ML frameworks (e. g., TensorFlow, PyTorch, Hugging Face).
- Familiarity with deploying AI/ML models on cloud platforms (e. g., AWS, Azure) with a focus on scalability.
- Excellent problem-solving skills and ability to work collaboratively in a dynamic, fast-paced environment.
Preferred Skills:
- Experience with MLOps for scaling and automating ML model deployment and management.
- Familiarity with graph databases and semantic search technologies.
- Understanding of AI-driven product development lifecycle, from data collection and model development to deployment and performance monitoring.
- Experience integrating AI models with CRM systems.