Role: Senior Associate – Conversational AI / RAG Engineer

Experience: 5+ Years

Location: Bangalore / Hyderabad

Notice Period: Immediate to 15 Days

Practice: AI Engineering & Intelligent Automation

Role Summary

We are looking for a highly skilled Conversational AI / RAG Engineer to design, build, and deploy enterprise-grade conversational AI solutions powered by Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Knowledge Intelligence platforms.

The ideal candidate will have hands-on experience building intelligent assistants, enterprise search solutions, knowledge bots, and AI copilots that leverage internal and external data sources to deliver accurate, context-aware responses. This role requires robust expertise in AI engineering, information retrieval, prompt engineering, vector databases, and cloud-native application development.

Key Responsibilities

- Conversational AI Development
- Design and develop conversational AI applications, virtual assistants, and enterprise copilots.
- Build multi-turn conversational experiences with contextual memory and personalization.
- Develop intent-based and GenAI-powered chatbot solutions.
- Integrate conversational AI solutions with enterprise applications, APIs, and business workflows.
- Optimize conversation flows, response quality, and user experience.
- Retrieval-Augmented Generation (RAG)
- Design and implement end-to-end RAG architectures.
- Build document ingestion, chunking, embedding, indexing, and retrieval pipelines.
- Implement semantic search and knowledge retrieval solutions across structured and unstructured data.
- Optimize retrieval relevance, grounding accuracy, and response quality.
- Develop scalable enterprise knowledge assistants leveraging internal content repositories.
- LLM Integration & Prompt Engineering
- Integrate OpenAI, Azure OpenAI, Gemini, Claude, and open-source models into enterprise applications.
- Design prompt templates and orchestration workflows.




- Develop strategies to minimize hallucinations and improve answer fidelity.
- Conduct prompt tuning and response evaluation to enhance system performance.
- AI Application Engineering
- Build AI microservices and APIs using Python and FastAPI.
- Develop scalable and secure backend systems supporting AI workloads.
- Implement authentication, authorization, and enterprise security standards.
- Optimize inference performance and operational efficiency.
- Evaluation, Monitoring & Governance
- Implement monitoring and observability mechanisms for conversational AI applications.
- Track retrieval performance, response quality, latency, user feedback, and model usage.
- Develop evaluation frameworks for grounding, correctness, relevance, and user satisfaction.
- Follow Responsible AI, security, privacy, and governance standards.

Required Skills

- Conversational AI
- Enterprise Chatbots
- Virtual Assistants
- AI Copilots
- Multi-turn Conversations
- Conversational Design
- Context Management
- RAG & Knowledge Systems
- Retrieval-Augmented Generation (RAG)
- Semantic Search
- Embedding Models
- Hybrid Search
- Knowledge Retrieval
- Vector Search Optimization
- AI / LLM Frameworks
- LangChain
- LangGraph
- LlamaIndex
- Semantic Kernel
- CrewAI (Preferred)
- Programming
- Python
- FastAPI
- REST APIs
- SQL
- Git
- Vector Databases & Search
- Pinecone
- Qdrant
- Weaviate
- PGVector
- FAISS
- Azure AI Search / Elasticsearch
- Cloud Platforms
- Azure OpenAI / Azure AI Foundry
- AWS Bedrock
- Google Vertex AI

Preferred Qualifications

- Experience building enterprise knowledge assistants.
- Experience integrating SharePoint, Microsoft 365, Confluence, ServiceNow, Salesforce, or other enterprise knowledge sources.
- Knowledge of Agentic AI and multi-agent architectures.
- Experience with LLMOps, evaluation frameworks, and AI observability tools.
- Cloud certifications (Azure/AWS/GCP).
- Experience deploying containerized AI applications using Docker and Kubernetes.

📌 Senior Associate – Conversational AI / RAG Engineer (Bengaluru)
🏢 Teamware Solutions
📍 Bengaluru