IT Application Support Specialist L2 (Agentic AI Engineer)

Key ResponsibilitiesGemini 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.QualificationsPreferred / 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 SummaryCategoryTools / TechnologiesAgent Development (Primary)Gemini Enterprise (Agent space), Gemini Models, Vertex AI, Model Armor, Custom ADK agents, Agent designerAI GovernanceEvaluation frameworks, audit/logging tooling, responsible-AI guardrails, IAM/access policiesRetrieval & DataRAG pipelines, enterprise search, data connectors, vector searchLanguagesPython (primary), Terraform, JavaScript, BashCloud Platform (Supporting)Google Cloud Platform (GCP) — IAM, Cloud Storage, networking basicsMonitoringCloud Monitoring, Cloud Logging, Prometheus, Grafana