Consultant- Python AI Developer

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.

Job Description: AI Engineer (Vertex AI Search & Prompt Engineering)

Position Title

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

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.

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 governance standards.

Support migration and onboarding of legacy knowledge repositories.

Security & Responsible AI

Implement enterprise AI governance 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

AI Evaluation Frameworks

Metadata Generation

Text Classification

Semantic Search

Google Cloud & Vertex AI

Vertex AI Search

Vertex AI Studio

Vertex AI Embeddings

Vertex AI APIs

Google Cloud Storage (GCS)

Cloud Run

Cloud Functions

IAM & Security Controls

The project's technology stack includes Vertex AI Search, cloud-native deployment, and AI-driven metadata extraction.

Search & Retrieval Technologies

Enterprise Search

Semantic Search

Vector Databases

Embeddings

RAG Architectures

Relevance Ranking

Search Indexing

Programming & Development

Python

Java

REST APIs

JSON

Spring Boot Integration

Git

CI/CD Pipelines

Data Technologies

PostgreSQL

Metadata Management

Content Repositories

Document Processing

Taxonomy Management

Preferred Qualifications

Experience implementing Knowledge Management Systems (KMS).

Experience with enterprise search platforms.

Experience with vector search technologies.

Familiarity with agentic AI solutions and AI assistants.

Experience in healthcare, insurance, or regulated industries.

Google Cloud Professional certifications.

Experience with Retrieval-Augmented Generation (RAG) frameworks.

Soft Skills

Strong analytical and problem-solving capabilities.

Excellent communication and stakeholder engagement skills.

Ability to explain AI concepts to technical and business audiences.

Strong experimentation mindset.

Passion for innovation and emerging AI technologies.

Key Deliverables

Vertex AI Search Configuration

Search Indexing Strategy

Prompt Libraries for Auto-Tagging

Metadata Generation Framework

AI Quality & Evaluation Reports

RAG Architecture Components

Content Classification Models

AI Governance Documentation

Success Measures

The successful candidate will:

Deliver highly relevant and accurate enterprise search experiences.

Improve metadata quality through automated AI tagging.

Increase content discoverability across the organization.

Reduce manual content classification effort.

Enable AI-powered knowledge retrieval and recommendation capabilities.

Establish scalable prompt engineering and governance practices.

Primary Technology Stack: Vertex AI Search • Generative AI • Prompt Engineering • Auto-Tagging • Enterprise Search • RAG • Vector Search • Python • GCP • Knowledge Management Systems.

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