Responsibilities 1. RAG Architecture & Pipeline Design § Design and maintain RAG that connects CRM to LLM to provide grounded, fact bases AI responses. 1.

Vector

Search & NLP § Implement and maintain vector embeddings for entities to enable semantic search and integration with LLM for generative AI features.

- MCP Server Development § Design and deploy MCP to bridge LLM with existing Data sources for real-time Retrieval.
- Generative AI Integration § Utilize LLMs to synthesize retrieved data into accurate, user friendly natural language responses. 1.

Performance

Optimization § Evaluate and optimize the end-to-end latency of search and generation.

- Data Analysis § Collect, analyze, and interpret large datasets to identify trends, patterns, and insights that can inform business decisions. 1.

Technical

Leadership § 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. 1.

Project

Coordination and Stakeholder Collaboration § Collaborate with project managers, business analysts, and stakeholders to understand business requirements and translate them into technical solutions. 1.

Quality

Assurance and testing § Collaborate with QA teams to validate data quality, ensure accuracy, and troubleshoot issues identified during testing. 1.

Continuous

Improvement § Stay up-to on the latest tools, techniques, and best practices. § Identify opportunities for process improvements, optimizations, and automation.

- Documentation and Standards § Ensure adherence to data governance and regulatory requirements, including security and compliance standards.

Contributing

Responsibilities - Design, develop,



implement and maintain AI/LLM products to solve specific business use cases.

- 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.
- Design and maintain RAG that connects CRM to LLM to provide grounded, fact bases AI responses.
- Explore and understand the CRM data, including customer demographics, behavior, and transactional data.
- Develop and train predictive models using various machine learning algorithms and techniques.
- Deploy models in production environments, such as CRM systems.
- Monitor model performance, identify areas for improvement, and retrain models as necessary.
- Generate insights and recommendations based on data analysis and modeling results.
- Communicate insights and results to stakeholders, including business leaders.
- Identify opportunities to improve CRM data quality, processes, and systems.
- Collaborate with project managers, business analysts, and stakeholders to understand business requirements and translate them into technical solutions.
- Stay current with industry trends, recent technologies, and emerging methodologies in data science and CRM.
- 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:
- Python
- Sql/Pl-Sql
- Machine learning algorithms
- RAG / Architecture
- 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

📌 Senior Software Engineer (Mumbai)
🏢 Infotel UK
📍 Mumbai