AI/ML Engineer

Experience: 5+ Years
Location: Bangalore, India (Hybrid)
Company: Clientell – Your ultimate RevOps and CRM dream team

About Clientell:
At Clientell, we transform RevOps through unbeatable CRM implementations, liberating your team’s bandwidth and unlocking pristine data insights. We’re seeking talented engineers to create AI agents that enhance the Salesforce ecosystem, driving efficiency and intelligence across CRM operations.

Key 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.

Qualifications:

• 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