Generative AI, VertexAI, Google ADK, AgenticAI, RAG Pipeline

Virtual drive on 20th June.

Generative AI, VertexAI, Google ADK, AgenticAI, RAG Pipeline

GenWe are looking for a skilled Generative AI Engineer to design, develop, and deploy enterprise-grade AI applications using Google Cloud technologies. The ideal candidate should have hands-on experience with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Vertex AI, Google Agent Development Kit (ADK), vector databases, and cloud-native AI architectures.

Key Responsibilities

Design and develop Generative AI solutions using Google Cloud Vertex AI.

Build and deploy AI agents using Google ADK (Agent Development Kit).

Develop and optimize Retrieval-Augmented Generation (RAG) pipelines for enterprise knowledge retrieval.

Integrate LLMs such as Gemini, OpenAI, Claude, and open-source models.

Create scalable prompt engineering and prompt management frameworks.

Build document ingestion, chunking, embedding, indexing, and retrieval pipelines.

Design multi-agent systems for workflow automation and intelligent decision-making.

Implement vector databases and semantic search solutions.

Develop APIs and microservices to integrate AI applications with enterprise systems.

Monitor model performance, latency, costs, and AI quality metrics.

Implement AI governance, security, and responsible AI practices.

Collaborate with product, data engineering, and cloud teams to deliver AI-driven solutions.

Required Skills

Generative AI

Large Language Models (LLMs)

Prompt Engineering

RAG Architecture

AI Agents and Agentic Workflows

Function Calling / Tool Use

Fine-Tuning Concepts

AI Evaluation Frameworks

Google Cloud

Vertex AI

Gemini Models

Vertex AI Agent Builder

Vertex AI Search

Vertex AI Pipelines

Cloud Storage

BigQuery

Cloud Functions

Cloud Run

Agent Development

Google Agent Development Kit (ADK)

Multi-Agent Systems

Agent Orchestration

Tool Integration

Workflow Automation

RAG & Vector Search

RAG Pipeline Design

Embedding Models

Document Chunking Strategies

Semantic Search

Vector Databases (Pinecone, Weaviate, ChromaDB, Vertex AI Vector Search)

Knowledge Graph Concepts

Programming

Python

FastAPI / Flask

REST APIs

LangChain

LangGraph

LlamaIndex

SQL

Cloud & DevOps

Google Cloud Platform (GCP)

Docker

Kubernetes

CI/CD Pipelines

Git

Infrastructure as Code

Preferred Skills

Experience with Gemini 2.x models.

Knowledge of MCP (Model Context Protocol).

Experience building conversational AI and enterprise copilots.

Experience with document intelligence and OCR pipelines.

Exposure to MLOps and LLMOps practices.