Senior Associate Conversational AI RAG Engineer

Elfonze Technologies; Hyderabad; On-site; INR 2,500,000 - 4,000,000; Full time

Job summary

Elfonze Technologies in Hyderabad is seeking a Senior Associate - Conversational AI / RAG Engineer to design, build, and deploy enterprise-grade AI solutions using LLMs, RAG, and knowledge platforms.

You will work on conversational assistants, enterprise search, and knowledge copilots with focus on retrieval, vector databases, prompt engineering, and cloud-native development.

Qualifications

• Experience building enterprise knowledge assistants.
• Experience integrating enterprise data sources (SharePoint, Confluence, Salesforce, etc.).
• Knowledge of LLMOps, evaluation and observability tooling.

Responsibilities

• Design and develop conversational AI applications and enterprise copilots.
• Build multi-turn experiences with memory and personalization.
• Develop GenAI-powered chatbot solutions and integration with enterprise apps.
• Optimize conversation flows, response quality, and UX.
• Design end-to-end RAG architectures and retrieval pipelines.
• Implement semantic search and knowledge retrieval across data sources.
• Integrate OpenAI, Azure OpenAI, Gemini, Claude and open-source models.

Skills

Conversational AI

Enterprise Chatbots

RAG & Knowledge Systems

Prompt Engineering

Python

FastAPI

Vector Databases

LangChain

Cloud Platforms

Azure OpenAI

AWS Bedrock

GCP Vertex AI

Pinecone

Qdrant

Weaviate

PGVector

FAISS

Tools

Pinecone

Qdrant

Weaviate

PGVector

FAISS

Azure OpenAI

Elasticsearch

Job description

Job Description

Role: Senior Associate - Conversational AI / RAG Engineer

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 AI

Requirements

• 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.