Job Summary The internal project staffing plan identifies an AI Engineer role whose primary responsibilities include "Vertex AI Search setup" and "prompt tuning for auto-tagging" as part of the AI-enabled Knowledge Management platform implementation.

We are seeking a skilled AI Engineer to design, implement, and optimize AI-driven search and knowledge discovery solutions. The primary focus of this role will be Vertex AI Search implementation, prompt engineering, and auto-tagging optimization for enterprise knowledge management and content discovery platforms.

The ideal candidate will work at the intersection of AI, search, metadata management, and enterprise applications, enabling intelligent search experiences, automated content classification, and Retrieval-Augmented Generation (RAG) capabilities.

Position Title

- AI Engineer (Vertex AI Search Prompt Engineering)
- AI Engineer (Generative AI Search Solutions)

Experience Required
- 5-8 Years of Relevant Experience
- (Including 2-4 years working with AI/ML, LLMs, Search, or Generative AI platforms)

Employment Type Full time

Role Summary Key Responsibilities

- Vertex AI Search Implementation
- Design and configure Vertex AI Search for enterprise document repositories.
- Build search indexing strategies for structured and unstructured content.
- Configure semantic search and relevance ranking mechanisms.
- Support content ingestion and indexing pipelines.
- Optimize search quality, response accuracy, and retrieval performance.

- The Knowledge Management initiative specifically includes Vertex AI Search setup as a core responsibility of the AI Engineer role.
- Prompt Engineering Auto-Tagging

- Design, test, and optimize prompts for automated metadata generation.




- Develop prompt patterns for: Content categorization; Topic extraction; Keyword generation; Knowledge classification; Document summarization; Metadata enrichment
- Improve precision and consistency of AI-generated tags.
- Establish prompt evaluation and tuning frameworks.
- Measure and improve tagging accuracy using feedback loops.

- The project documentation references Vertex AI-based auto-tagging and metadata generation capabilities as part of the AI-enabled knowledge platform.
- AI-Powered Content Management

- Develop AI workflows for automatic content classification.
- Build metadata extraction pipelines for enterprise documents.
- Implement automated document tagging and taxonomy alignment.
- Support knowledge discovery and enterprise search experiences.
- Improve content findability and search relevance.

- RAG (Retrieval-Augmented Generation) Solutions

- Design and implement RAG architectures.
- Configure embedding generation and vector indexing.
- Develop retrieval pipelines supporting AI assistants and enterprise copilots.
- Improve context retrieval for Generative AI applications.
- Optimize document chunking and retrieval strategies.

- The initiative includes vector indexing and AI-powered knowledge retrieval capabilities.
- AI Platform Integration

- Integrate AI capabilities with existing enterprise applications.




- Collaborate with backend teams to expose AI services through APIs.
- Work closely with product teams to embed AI features into business workflows.
- Support AI-powered search, recommendations, and content discovery experiences.

- Model Evaluation Optimization

- Evaluate prompt effectiveness and model outputs.
- Establish AI quality metrics and performance benchmarks.
- Identify hallucination risks and implement mitigation mechanisms.
- Develop validation frameworks for automated content processing.
- Monitor AI service utilization and performance.

- Data Content Engineering

- Support content ingestion and preprocessing pipelines.
- Prepare data for indexing and AI processing.
- Create content transformation workflows.
- Ensure metadata quality and goverce standards.
- Support migration and onboarding of legacy knowledge repositories.

- Security Responsible AI

- Implement enterprise AI goverce standards.
- Ensure secure handling of proprietary and sensitive content.
- Follow Responsible AI practices for transparency and fairness.
- Support compliance and auditability requirements.

Required Qualifications
- Experience
- 5-8 years of software engineering, AI engineering, search engineering, or machine learning experience.
- Hands-on experience implementing Generative AI solutions.
- Experience with AI search platforms and enterprise content discovery.
- Experience with LLM prompt engineering and optimization.
- Experience designing AI-enabled document processing solutions.

Technical Skills
- AI Machine Learning
- Generative AI
- Large Language Models (LLMs)
- Prompt Engineering
- Prompt Optimization

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