The posting
Key Responsibilities
Gemini Enterprise Agent Development
- Design, build, and own multi-agent solutions on Gemini Enterprise (formerly Agent space) — 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 structure devaluation 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 dash boards 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.
Required Skills & Experience
- 7+ years of experience in AI/ML engineering, applied AI, or Gen AI platform roles, with hands-on ownership of agent or LLM-application development.
- Direct, hands-on experience building and configuring agents on Gemini Enterprise (or Agent space) — 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 CloudPlatform (GCP) — IAM, Cloud Storage, basic networking — sufficient to collaborate with platform/infrastructure teams.
Qualifications
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 Gen AI /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
Category
Tools / Technologies
Agent Development (Primary)
Gemini Enterprise (Agent space), 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
