Posted 15 June, 2026
ARF L3 GDC AI Engineer
NR Consulting - India
REMOTE, IN
Full Time
Reference: 26-06499-2220-1
Title: ARF L3 GDC AI Engineer
Location: REMOTE
Exp: 8+ Years
Job Description:
Core Responsibilities
• AI Model & Pipeline Implementation: Act as the primary developer for deploying and maintaining AI models and engineering their associated ingestion pipelines.
• Vertex AI Search Deployment: Implement a dual-search experience that allows users to utilize both a Vertex AI-powered semantic discovery engine and a traditional faceted keyword search.
• Conversational Search Optimization: Develop single-session conversational search functions featuring generative summaries, ensuring strict configuration to index only proprietary content with precise source citations to eliminate open-internet hallucinations.
• Intelligent Asset Tagging: Leverage Gemini multimodal models during the ingestion workflow to parse raw, unstructured data (such as PDFs and presentations) and automatically extract/suggest library-science-level metadata tags.
• RAG Chatbot Development: Construct and deploy isolated Website Assistant Chatbots using Vertex AI Search and Conversation Agent/Agent Builder to handle Retrieval-Augmented Generation (RAG) based on crawled website and Member Center content.
• AI Guardrails & Performance Control: Implement backend controls for tone, persona alignment, temporal weighting (boosting/burying content), token consumption caps, and rate-limiting parameters to manage platform costs and security.
• Cross-Functional Integration: Collaborate with cloud and data engineers to stream full inference logs to Google Cloud Storage (GCS) and BigQuery, and ensure proper interface coordination with custom React middleware, Salesforce RBAC permissions, and WordPress sync mechanisms.
• Collaboration Framework: Participates in daily scrum ceremonies, technical design review sessions, and rigorous User Acceptance Testing (UAT) quality logging.
Technical Skills & Qualifications
Required AI/ML Expertise
• Google Cloud Vertex AI: Hands-on experience deploying Vertex AI Search and Vertex AI Conversation / Agent Builder.
• Large Language Models: Direct experience working with Gemini models for multimodal text parsing, supervised fine tuning, and automated metadata extraction.
• Search & Retrieval Architectures: Deep understanding of Retrieval-Augmented Generation (RAG) frameworks, semantic discovery, and tuning search metrics like recall precision, chunking quality, and latency.
Systems & Cloud Infrastructure
• GCP Architecture Ecosystem: Familiarity with deploying services across Cloud Run, Firestore, Cloud Workflows, Google Cloud Storage (GCS), and Eventarc.
• Data Pipelines: Experience working with structured data warehouses like BigQuery and streaming inference/activity logs for analytics.
Preferred Integration Experience
• Downstream Systems: Understanding of how AI APIs interact with external systems, specifically mapping relational metadata to Salesforce canonical IDs and syncing assets with WordPress.
Location: REMOTE
Exp: 8+ Years
Job Description:
Core Responsibilities
• AI Model & Pipeline Implementation: Act as the primary developer for deploying and maintaining AI models and engineering their associated ingestion pipelines.
• Vertex AI Search Deployment: Implement a dual-search experience that allows users to utilize both a Vertex AI-powered semantic discovery engine and a traditional faceted keyword search.
• Conversational Search Optimization: Develop single-session conversational search functions featuring generative summaries, ensuring strict configuration to index only proprietary content with precise source citations to eliminate open-internet hallucinations.
• Intelligent Asset Tagging: Leverage Gemini multimodal models during the ingestion workflow to parse raw, unstructured data (such as PDFs and presentations) and automatically extract/suggest library-science-level metadata tags.
• RAG Chatbot Development: Construct and deploy isolated Website Assistant Chatbots using Vertex AI Search and Conversation Agent/Agent Builder to handle Retrieval-Augmented Generation (RAG) based on crawled website and Member Center content.
• AI Guardrails & Performance Control: Implement backend controls for tone, persona alignment, temporal weighting (boosting/burying content), token consumption caps, and rate-limiting parameters to manage platform costs and security.
• Cross-Functional Integration: Collaborate with cloud and data engineers to stream full inference logs to Google Cloud Storage (GCS) and BigQuery, and ensure proper interface coordination with custom React middleware, Salesforce RBAC permissions, and WordPress sync mechanisms.
• Collaboration Framework: Participates in daily scrum ceremonies, technical design review sessions, and rigorous User Acceptance Testing (UAT) quality logging.
Technical Skills & Qualifications
Required AI/ML Expertise
• Google Cloud Vertex AI: Hands-on experience deploying Vertex AI Search and Vertex AI Conversation / Agent Builder.
• Large Language Models: Direct experience working with Gemini models for multimodal text parsing, supervised fine tuning, and automated metadata extraction.
• Search & Retrieval Architectures: Deep understanding of Retrieval-Augmented Generation (RAG) frameworks, semantic discovery, and tuning search metrics like recall precision, chunking quality, and latency.
Systems & Cloud Infrastructure
• GCP Architecture Ecosystem: Familiarity with deploying services across Cloud Run, Firestore, Cloud Workflows, Google Cloud Storage (GCS), and Eventarc.
• Data Pipelines: Experience working with structured data warehouses like BigQuery and streaming inference/activity logs for analytics.
Preferred Integration Experience
• Downstream Systems: Understanding of how AI APIs interact with external systems, specifically mapping relational metadata to Salesforce canonical IDs and syncing assets with WordPress.
