As a Gemini Enterprise AI Engineer, you will design, build, scale, and productionize agentic systems powered by Google's Gemini Enterprise platform—the unified, enterprise-grade front-door for AI agents, deep enterprise search, and multimodal reasoning.

You will transform business friction (fragmented data, slow research, manual workflows) into high-ROI autonomous agents that operate across Google Workspace, Salesforce, SAP, and BigQuery, ensuring every deployment meets rigorous enterprise security and governance standards.

Job Title: \Gemini Enterprise AI Engineer / Agentspace AI Engineer

Duration: Full-Time

Location: Hyderabad

Working hours : 2:00 PM IST to 11:00 PM IST

No of Openings: 2

Key Responsibilities:

• Agent Orchestration: Architect custom multi-agent systems using Gemini Enterprise, Vertex AI Agent Builder, and the Agent Development Kit (ADK).
• Enterprise Grounding: Build secure pipelines to integrate Gemini with internal data using Vertex AI Search.
• Scale RAG Architectures: Design RAG systems that combine structured data and real-time APIs with multimodal content (docs, images, and spreadsheets).
• Enablement: Create reusable no-code agent templates within the Gemini Enterprise Workbench .
• Production Guardrails: Own the reliability and safety of agent workloads, including function calling, PII redaction, and hallucination mitigation.
• Governance: Partner with Security teams to implement enterprise controls, including IAM, VPC Service Controls (VPC-SC), and data residency.
• Optimization: Track and improve Agent ROI using metrics like task completion rate, latency, and cost-per-inference.

Qualifications:

• AI Engineering Depth: 2–3+ years specifically shipping production-grade GenAI/LLM systems.
• Google AI Stack: Deep proficiency in Vertex AI, Gemini 1.5/2.0+ (Pro & Flash), and the Vertex Agent Engine.
• Agentic Patterns: Proven experience with ReAct, Chain-of-Thought, Tool-Use (Function Calling), and stateful orchestration.
• Core Technical Stack: Advanced Python and experience with Enterprise APIs (OAuth, REST, GraphQL).
• Data & Search: Experience with BigQuery, Vector Databases, and Vertex AI Search & Conversation
• Enterprise Security: Familiarity with Cloud KMS/CMEK, DLP API, and audit logging within a cloud environment.

Differentiators (Nice-to-Have)

• Specific Platform Experience: Prior deployments using Gemini Enterprise or LangGraph for hierarchical agent swarms.
• Multimodal Expertise: Experience building agents that process video, complex document layouts, or real-time audio.

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