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