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