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

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

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

o 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.

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

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

. AI Governance & Responsible Agent Operations

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

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

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

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

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

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

. Cross-Functional Collaboration & Automation

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

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

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

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

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

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

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

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

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

. Tech Stack Summary

o Agent Development (Primary)

o Gemini Enterprise (Agentspace)

o Gemini Models

o Vertex AI

o Model Armor

o Custom ADK agents

o Agent designer

. AI Governance

o Evaluation frameworks

o Audit/logging tooling

o Responsible-AI guardrails

o IAM/access policies

. Retrieval & Data

o RAG pipelines

o Enterprise search

o Data connectors

o Vector search

. Languages

o Python (primary)

o Terraform

o JavaScript

o Bash

. Cloud Platform (Supporting)

o Google Cloud Platform (GCP) - IAM, Cloud Storage, networking basics

. Monitoring

o Cloud Monitoring

o Cloud Logging

o Prometheus

o Grafana