Senior Associate – Conversational AI / RAG Engineer
Elfonze Technologies Private LimitedMatch Score
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Protocol Intelligence
Data-driven signals on your job's competitivenessNiche Conversational AI/RAG skillset and small employer reduce applicant density despite metro location.
Specialized LLM/RAG skills transferable across sectors, but enterprise integrations demand domain familiarity.
Extensive mandatory LLM, RAG, vector DB, and cloud/tooling requirements enforce strict technical filters.
Job Description
Structured overview of role & requirementsAbout This Role
Design, build, and deploy enterprise-grade conversational AI and Retrieval-Augmented Generation (RAG) solutions using large language models and knowledge platforms.
Develop and integrate multi-turn conversational experiences, AI copilots, and enterprise chatbots with internal and external data sources and enterprise workflows.
Engineer scalable AI microservices with Python/FastAPI, implement monitoring, evaluation, and governance frameworks focusing on response quality, security, and Responsible AI standards.
Minimum Requirements
Hands-on experience with Conversational AI, Retrieval-Augmented Generation, and vector database/search technologies (e.g., Pinecone, Qdrant).
Proficient in Python programming and building AI microservices using FastAPI, including REST APIs and cloud-native applications.
Experience integrating enterprise knowledge sources such as SharePoint, Microsoft 365, Confluence, and ServiceNow.
Work Experience Required: Not explicitly mentioned in the JD.
Ideal Candidate Profile
Experience deploying and managing enterprise knowledge assistants with strong skills in LLM integration, prompt engineering, and reducing hallucinations.
Familiarity with cloud AI platforms (Azure OpenAI, AWS Bedrock, Google Vertex AI) and containerization using Docker and Kubernetes.
Competent in AI security, observability, evaluation frameworks and Responsible AI governance for scalable enterprise AI applications.