AI Engineer (Agentic AI)
22 hours ago
Saint Austell
·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 • Gemini Enterprise Agent Development, • oDesign, build, and own multi-agent solutions on Gemini Enterprise (formerly Agentspace) — agent architecture, orchestration, task/tool design, and multi-turn conversation flows., • oConfigure grounding, enterprise search, and data connectors so agents retrieve accurate, up-to-date, and properly-scoped information., • oIntegrate 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., • oDesign and productionize RAG pipelines and retrieval strategies that feed agent grounding sources., • oContinuously evaluate and iterate on agent quality — accuracy, relevance, latency, and cost — using structured evaluation frameworks., • AI Governance & Responsible Agent Operations, • oDefine and implement governance frameworks for Gemini Enterprise agents: access control, data permissions, usage policies, and approval workflows for new agents going into production., • oBuild guardrails against hallucination, data leakage, prompt injection, and unauthorized data access across agents and connectors., • oEstablish monitoring, logging, and audit trails for agent behavior, including token usage, response quality, and policy violations., • oPartner with security, legal, and compliance stakeholders to ensure agents meet data privacy, residency, and responsible‑AI requirements., • oCreate and maintain documentation, review checklists, and lifecycle standards (build → evaluate → approve → monitor → retire) for enterprise agents., • oTrack Gemini model and Gemini Enterprise feature releases and assess their impact on existing agents and governance policies., • Cross-Functional Collaboration & Automation, • oWork closely with data science, AI engineering, security, and business teams to translate use cases into governed, production-ready agents., • oAutomate agent configuration, evaluation, and deployment workflows using Python and APIs/SDKs for Gemini Enterprise and Vertex AI., • oBuild internal tooling and dashboards to give stakeholders visibility into agent inventory, usage, and governance status., • oParticipate in code and design reviews, contributing to shared standards for agent development and governance., • 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., • 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., • oStrong governance and risk mindset — able to balance agent capability with safety, compliance, and trust., • oClear communicator who can translate technical agent behavior into business and compliance language., • oComfortable operating in ambiguity, especially with fast-evolving Gemini features and emerging agent governance practices., • oOwnership mentality — from agent design through deployment, evaluation, and long-term governance., • Agent Development (Primary), • oGemini Enterprise (Agentspace), • oGemini Models, • oVertex AI, • oModel Armor, • oCustom ADK agents, • oAgent designer, • AI Governance, • oEvaluation frameworks, • oAudit/logging tooling, • oResponsible‑AI guardrails, • oIAM/access policies, • Retrieval & Data, • oRAG pipelines, • oEnterprise search, • oData connectors, • oVector search, • Languages, • oPython (primary), • oTerraform, • oJavaScript, • oBash, • Cloud Platform (Supporting), • oGoogle Cloud Platform (GCP) — IAM, Cloud Storage, networking basics, • Monitoring, • oCloud Monitoring, • oCloud Logging, • oPrometheus, • oGrafana #J-18808-Ljbffr