AI Engineer - Conversational AI
- Compensation
- Not disclosed
- Location
- Mumbai
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 2+ years
- Category
- Ai engineering
- Posted
Skills
Full description
Role Title : AI Engineers
Department : AI CoE / Conversational AI Team
Location : Mumbai
Notice Period : Immediate Joiners
Exp range : 2 - 4 Years
Role Summary :
We are seeking an AI Engineer to design, develop, test, and optimize enterprise-grade conversational AI and Agentic AI solutions for banking use cases. The role involves working on LLM-powered chatbots, AI copilots, RAG systems, prompt engineering, conversation design, and multi-agent workflows.
The candidate will collaborate with business, engineering, product, and governance teams to deliver scalable AI systems that enhance customer experience, employee productivity, and operational efficiency.
This role is suitable for professionals with capabilities across conversational AI, prompt engineering, chatbot development, AI workflow orchestration, and AI evaluation.
Key Responsibilities : 1. Conversational AI Development :
- Build and enhance AI-powered chatbots and virtual assistants
- Develop customer-facing and employee-facing conversational AI systems
- Design contextual and personalized conversational experiences
- Improve chatbot accuracy, intent handling, and response quality
2. Agentic AI & LLM Engineering :
- Develop AI agents capable of reasoning, planning, and tool usage
- Build multi-step AI workflows and autonomous task execution systems
- Implement memory-enabled AI interactions
- Integrate external tools, APIs, and enterprise systems into AI workflows
3. Prompt Engineering & Conversation Design :
- Design and optimize prompts for banking use cases
- Create structured conversational flows and dialogue logic
- Tune prompts for accuracy, safety, and business alignment
- Improve AI behavior through prompt experimentation and testing
4. RAG & Knowledge Systems :
- Build Retrieval-Augmented Generation (RAG) pipelines
- Integrate enterprise knowledge bases and document repositories
- Improve retrieval quality and grounding mechanisms
- Work with vector databases and semantic search systems
5. AI Evaluation & Quality Assurance :
- Conduct AI testing, benchmarking, and regression validation