Senior Engineer – Google Agentic AI (ADK, Agent Development & Deployment

This is a remote position.

Role : Senior Engineer – Google Agentic AI (ADK, Agent Development & Deployment

Work Location: Remote

No. of Internal interview:1

Client interview required:1

Job Description:

Position Overview

We are seeking a highly skilled Google Agentic AI Engineer to design, develop, deploy, and operate enterprise-grade AI agents using Google Agent Development Kit (ADK), Vertex AI Agent Builder, Gemini Models, and Google Cloud Platform (GCP). The candidate will be responsible for building intelligent, scalable, secure, and production-ready multi-agent systems that integrate with enterprise applications, APIs, and knowledge repositories.

Key Responsibilities

Agent Development

Design and develop AI agents using Google ADK.

Build autonomous and multi-agent workflows leveraging Gemini models.

Implement agent orchestration, memory management, session handling, and tool integrations.

Develop custom tools, function calling mechanisms, and API integrations for enterprise use cases.

Design agent collaboration patterns using A2A and MCP standards.

Build reusable agent templates and frameworks to accelerate solution delivery.

Agent Deployment & Operations

Deploy agents using Vertex AI Agent Builder and Agent Engine.

Build scalable production deployments on GCP services including Cloud Run, GKE, and Vertex AI.

Implement agent observability, monitoring, tracing, logging, and performance optimization.

Define SLIs, SLOs, and operational dashboards for AI workloads.

Support production operations, incident management, and continuous improvement initiatives.

Enterprise AI Solutions

Develop RAG solutions by leveraging Vertex AI Search, Vector Search, and enterprise knowledge sources.

Integrate agents with enterprise systems such as Salesforce, ServiceNow, SharePoint, Jira, Confluence, and custom APIs.

Implement context engineering, knowledge graph integration, and enterprise grounding techniques.

Build workflow automation agents, diagnostic agents, customer support assistants, and operational bots.

Security, Governance & Compliance

Design secure AI architectures following enterprise governance standards.

Implement guardrails, content filtering, hallucination detection, DLP, access control, and identity management.

Ensure compliance with enterprise security, privacy, and regulatory requirements.

Drive AI governance, monitoring, risk management, and responsible AI practices.

Engineering Excellence

Establish coding standards, evaluation frameworks, and testing strategies for AI agents.

Mentor engineering teams on Agentic AI architecture and development best practices.

Conduct architecture reviews and technical assessments.

Stay current with advancements in Agentic AI, LLMs, ADK, MCP, A2A, LangGraph, CrewAI, and related ecosystems.

Mandatory Skills

Google Agentic AI

Strong hands-on experience with:

Google Agent Development Kit (ADK)

Vertex AI

Vertex AI Agent Builder

Agent Engine

Gemini Models

Gemini API

Multi-Agent Systems

Agent Orchestration

Agent Memory & Sessions

Tool Calling and Function Calling

AI/LLM Engineering

Prompt Engineering

RAG Architecture

Vector Databases

Knowledge Graphs

Agent Evaluation Frameworks

LLM Fine-Tuning and Optimization

AI Observability and Monitoring

Cloud & Development

Google Cloud Platform (GCP)

Python

REST APIs

Kubernetes (GKE)

Cloud Run

Docker

GitHub Actions / CI-CD

Infrastructure as Code (Terraform preferred)

Data & Integration

BigQuery

Vertex AI Search

Vector Search

Enterprise API Integration

MCP and A2A Protocols

Preferred Skills

LangGraph

LangChain

CrewAI

LlamaIndex

OpenAI / Anthropic / Gemini ecosystems

AI Security & Governance

MLOps / LLMOps

Event-driven architecture

Real-time AI applications

Enterprise SaaS integrations

AI Cost Optimization

Qualifications

Bachelor's or master’s degree in computer science, Engineering, AI, Data Science, or related field.

10–15 years of software engineering experience.

Minimum 2–3 years of hands-on experience building GenAI, Agentic AI, or LLM-based solutions.

Google Cloud certifications preferred:

Professional Cloud Architect

Professional Machine Learning Engineer

Generative AI Leader/Engineer Certifications