Responsibilities
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RAG Architecture & Pipeline Design
§ Design and maintain RAG that connects CRM to LLM to provide grounded, fact bases AI responses.
§ Implement and maintain vector embeddings for entities to enable semantic search and integration with LLM for generative AI features.
§ Design and deploy MCP to bridge LLM with existing Data sources for real-time Retrieval.
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Generative AI Integration
§ Utilize LLMs to synthesize retrieved data into accurate, user friendly natural language responses.
§ Evaluate and optimize the end-to-end latency of search and generation.
§ Collect, analyze, and interpret large datasets to identify trends, patterns, and insights that can inform business decisions.
§ Lead code reviews, ensuring adherence to coding standards, best practices, and quality requirements.
§ Act as a technical point of contact for complex database issues, offering solutions and recommendations to resolve them.
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Project Coordination and Stakeholder Collaboration
§ Collaborate with project managers, business analysts, and stakeholders to understand business requirements and translate them into technical solutions.
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Quality Assurance and testing
§ Collaborate with QA teams to validate data quality, ensure accuracy, and troubleshoot issues identified during testing.
§ Stay up-to on the latest tools, techniques, and best practices.
§ Identify opportunities for process improvements, optimizations, and automation.
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Documentation and Standards
§ Ensure adherence to data governance and regulatory requirements, including security and compliance standards.
Contributing Responsibilities
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Design, develop, implement and maintain AI/LLM products to solve specific business use cases.
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Implement and maintain vector embeddings for entities to enable semantic search and integration with LLM for generative AI features.
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Design and deploy MCP to bridge LLM with existing Data sources for real-time Retrieval.
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Design and maintain RAG that connects CRM to LLM to provide grounded, fact bases AI responses.
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Explore and understand the CRM data, including customer demographics, behavior, and transactional data.
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Develop and train predictive models using various machine learning algorithms and techniques.
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Deploy models in production environments, such as CRM systems.
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Monitor model performance, identify areas for improvement, and retrain models as necessary.
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Generate insights and recommendations based on data analysis and modeling results.
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Communicate insights and results to stakeholders, including business leaders.
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Identify opportunities to improve CRM data quality, processes, and systems.
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Collaborate with project managers, business analysts, and stakeholders to understand business requirements and translate them into technical solutions.
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Stay current with industry trends, new technologies, and emerging methodologies in data science and CRM.
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Excellent Communication and Listening Skills, attention to details is must
Technical & Behavioral Competencies
Python, R, SQL
Machine learning algorithms
PyTorch or TensorFlow
Specific Qualifications:
Data Scientist
Skills Referential (Required knowledge, skills and abilities)
Technical Skills:
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Python
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Sql/Pl-Sql
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Machine learning algorithms
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RAG / Architecture
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CRM Schema
Behavioral Skills:
- Active listening
- Client focused
- Communication skills - oral & written
- Ability to deliver / Results driven
Education Level: Any Graduation/Post Graduation
Location: Mumbai
