Senior Associate – Conversational AI / RAG Engineer

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.