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.