Senior Associate – Conversational AI / RAG Engineer

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 strong 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 AIPreferred 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.