AI Engineer (Agentic AI)

Job Description ::

We are looking for a Gemini Enterprise Agent Engineer to lead the design, development, and governance of enterprise AI agents built on Gemini Enterprise and Gemini models. This role owns the full lifecycle of agent development — from architecture and grounding to deployment, evaluation, and ongoing governance — ensuring agents are accurate, safe, compliant, and trusted enough to run in production across the business.

You'll be the go-to expert for building agents on Gemini Enterprise (agent design, orchestration, grounding, connectors) and for establishing the governance frameworks — access control, evaluation, auditing, and responsible-AI guardrails — that keep those agents reliable at scale. Underlying cloud infrastructure runs on Google Cloud Platform (GCP), so working familiarity with GCP is helpful for collaborating with the platform team

Key Responsibilities::

Gemini Enterprise Agent Development

Design, build, and own multi-agent solutions on Gemini Enterprise (formerly Agentspace) — agent architecture, orchestration, task/tool design, and multi-turn conversation flows.

Configure grounding, enterprise search, and data connectors so agents retrieve accurate, up-to-date, and properly-scoped information.

Integrate Gemini models (via the Gemini API and/or Vertex AI) into agents and downstream applications, tuning prompts, context strategies, and tool/function calling for reliability.

Design and productionize RAG pipelines and retrieval strategies that feed agent grounding sources.

Continuously evaluate and iterate on agent quality — accuracy, relevance, latency, and cost — using structured evaluation frameworks.

AI Governance & Responsible Agent Operations

Define and implement governance frameworks for Gemini Enterprise agents: access control, data permissions, usage policies, and approval workflows for new agents going into production.

Build guardrails against hallucination, data leakage, prompt injection, and unauthorized data access across agents and connectors.

Establish monitoring, logging, and audit trails for agent behavior, including token usage, response quality, and policy violations.

Partner with security, legal, and compliance stakeholders to ensure agents meet data privacy, residency, and responsible-AI requirements.

Create and maintain documentation, review checklists, and lifecycle standards (build → evaluate → approve → monitor → retire) for enterprise agents.

Track Gemini model and Gemini Enterprise feature releases and assess their impact on existing agents and governance policies.

Cross-Functional Collaboration & Automation

Work closely with data science, AI engineering, security, and business teams to translate use cases into governed, production-ready agents.

Automate agent configuration, evaluation, and deployment workflows using Python and APIs/SDKs for Gemini Enterprise and Vertex AI.

Build internal tooling and dashboards to give stakeholders visibility into agent inventory, usage, and governance status.

Participate in code and design reviews, contributing to shared standards for agent development and governance.

Qualifications::

Required Skills & Experience

7+ years of experience in AI/ML engineering, applied AI, or GenAI platform roles, with hands-on ownership of agent or LLM-application development.

Direct, hands-on experience building and configuring agents on Gemini Enterprise (or Agentspace) — agent design, grounding, enterprise search, data connectors.

Strong hands-on experience with Gemini models (via Gemini API or Vertex AI) — prompting, tool/function calling, context and RAG design.

Practical experience implementing AI governance controls — access management, guardrails, evaluation frameworks, audit logging, and responsible-AI policies for LLM/agent systems.

Solid understanding of LLM application patterns — RAG, embeddings, vector search, multi-agent orchestration.

Solid Python programming skills for automation, evaluation tooling, and API integration.

Ability to work cross-functionally with data science, security/compliance, and business stakeholders to govern and scale agent deployments.

Working familiarity with Google Cloud Platform (GCP) — IAM, Cloud Storage, basic networking — sufficient to collaborate with platform/infrastructure teams.

Preferred / Nice-to-Have

Google Cloud certifications (Professional Machine Learning Engineer, or Professional Cloud Architect).

Experience with Terraform, Kubernetes (GKE), or CI/CD pipelines, for coordinating with platform/DevOps teams on agent infrastructure.

Experience with monitoring/observability stacks (Cloud Monitoring, Prometheus, Grafana, Datadog).

Familiarity with responsible-AI/model-risk frameworks applied to enterprise GenAI.

Prior experience with other enterprise GenAI/agent platforms (e.g., OpenAI, Anthropic, open-source LLM stacks) as a point of comparison.

Soft Skills

Strong governance and risk mindset — able to balance agent capability with safety, compliance, and trust.

Clear communicator who can translate technical agent behavior into business and compliance language.

Comfortable operating in ambiguity, especially with fast-evolving Gemini features and emerging agent governance practices.

Ownership mentality — from agent design through deployment, evaluation, and long-term governance.

Tech Stack Summary

Agent Development (Primary)

• Gemini Enterprise (Agentspace)

• Gemini Models

• Vertex AI

• Model Armor

• Custom ADK agents

• Agent designer

AI Governance

• Evaluation frameworks

• Audit/logging tooling

• Responsible-AI guardrails

• IAM/access policies

Retrieval & Data

• RAG pipelines

• Enterprise search

• Data connectors

• Vector search

Languages

• Python (primary)

• Terraform

• JavaScript

• Bash

Cloud Platform (Supporting)

• Google Cloud Platform (GCP) — IAM, Cloud Storage, networking basics

Monitoring

• Cloud Monitoring

• Cloud Logging

• Prometheus

• Grafana