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Elfonze Technologies
Elfonze Technologies

Senior Associate – Conversational AI / RAG Engineer

Hyderabad, Bangalore, India
Full-time
Posted 5d ago
3 views
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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

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.

Key Requirements & Skills

  • Conversational AI: enterprise chatbots, virtual assistants, AI copilots
  • Multi-turn conversations, conversational design, context management
  • Retrieval-Augmented Generation (RAG)
  • Semantic search
  • Embedding models
  • Hybrid search
  • Knowledge retrieval
  • Vector search optimization
  • LangChain
  • LangGraph
  • LlamaIndex
  • Semantic Kernel
  • CrewAI
  • Python
  • FastAPI
  • REST APIs
  • SQL
  • Git
  • Pinecone
  • Qdrant
  • Weaviate
  • PGVector
  • FAISS
  • Azure AI Search / Elasticsearch
  • Azure OpenAI / Azure AI Foundry
  • AWS Bedrock
  • Google Vertex AI
  • 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

Frequently Asked Questions

How to apply for Senior Associate – Conversational AI / RAG Engineer at Elfonze Technologies?

Click the "Apply on Company Website" button on this page to submit your application directly on the employer's official portal.

What is the salary for this role?

Salary details will be discussed during the interview.

What experience is required?

This position is open to freshers and experienced candidates.

Is this position still open?

Yes, currently active and accepting applications.

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Elfonze Technologies

Elfonze Technologies

Elfonze Technologies accelerates innovation through strategic IT consulting, product engineering, connected supply chain services, and staff augmentation—empowering businesses to thrive in a digital-first world.

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Senior Associate – Conversational AI / RAG Engineer

Elfonze Technologies · Hyderabad

Apply on Company Website