Job highlights

- Job Type. Full time.

- Required Skills
- 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

- Experience

- Experience building enterprise knowledge assistants.

Job Summary

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

- 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.

- 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.

Experience

- 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.

Job Type

Full time

Location

Telangana, India

Required Skills

- Conversational AI

- Enterprise Chatbots

- Virtual Assistants

- AI Copilots

- Multi-turn Conversations

- RAG Knowledge Systems

- Semantic Search

- Embedding Models

- LangChain

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Senior Associate - Conversational AI / RAG Engineer (Hyderabad)
🏢 ElfOnze Technologies
📍 Hyderabad