Position: AI/Gen AI Engineer Location: Chennai - Onsite/Hybrid/Remote Engagement Type: Full Time Shift Timing: 1.30PM - 10.30PM IST (UK Shift Time) About the Role: We are hiring an AI / GenAI Engineer to build and deploy production agents on Google Vertex AI (now the Gemini Enterprise Agent Platform). You will work end-to-end — from prototyping with Gemini models and RAG, to building multi-agent workflows with the Agent Development Kit (ADK), to deploying on Agent Runtime with proper governance, evaluation, and monitoring. This is a hands-on engineering role with direct client and solution-design exposure. Key Responsibilities: Design, build, and deploy enterprise-grade AI agents on Vertex AI / Gemini Enterprise Agent Platform (Model Garden, Agent Builder / Agent Studio, Agent Engine / Agent Runtime, Vertex AI Pipelines, Model Registry, Endpoints). Develop GenAI applications using Gemini (3.x family) and other Model Garden models — prompt engineering, function calling, structured output, and model evaluation/tuning. Build RAG pipelines using Vertex AI Search, Vector Search, RAG Engine, and embeddings; implement grounding against enterprise data sources. Implement multi-agent orchestration using the Agent Development Kit (ADK), Memory Bank / Sessions, and the Agent2Agent (A2A) protocol; integrate tools via Model Context Protocol (MCP) servers. Productionize models and agents with solid MLOps practices: CI/CD, Vertex AI Pipelines, model/agent monitoring, observability, and cost optimization. Integrate AI services with enterprise systems and APIs (REST/gRPC, FastAPI) and wider GCP services (BigQuery, Cloud Run, Cloud Functions, Pub/Sub, GCS, Dataflow, GKE). Apply enterprise governance and security — IAM, data privacy, and responsible-AI guardrails. Partner with solution architects and client stakeholders to gather requirements, design solutions, and document deliverables. Required Skills & Qualifications: 4–8 years of software/ML engineering experience, with hands-on Vertex AI / Gemini Enterprise Agent Platform (or equivalent GCP AI) project experience. Strong Python programming; clean, production-quality code. Practical experience building GenAI / LLM applications — prompting, RAG, embeddings, vector databases, and model evaluation. Working knowledge of Google Cloud Platform core services (BigQuery, Cloud Run/Functions, GCS, IAM, Pub/Sub). Experience deploying and operating models/agents in production (MLOps, containerization with Docker, basic Kubernetes/GKE). Solid understanding of agentic AI concepts — tool use, multi-agent orchestration, memory, and agent governance. Strong API design and integration skills (REST/gRPC, FastAPI). Good communication and the ability to work directly with clients and cross-functional teams. Good-to-Have (Preferred): Google Cloud certification — Professional Machine Learning Engineer or Professional Cloud Architect. Experience with the Agent Development Kit (ADK), A2A protocol, and MCP integrations. Familiarity with orchestration frameworks such as LangGraph, LangChain, or LlamaIndex. Exposure to other AI stacks (AWS Bedrock, Azure OpenAI, Anthropic Claude) for multi-cloud / multi-model work. Experience integrating AI with enterprise platforms (NetSuite, Salesforce, ERP/CRM systems). Background in consulting / client-facing delivery environments. Education: Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field (B.E. / B.Tech / M.E. / M.Tech / MCA). Equivalent practical experience also considered.