Architect - Google Vertex AI & Databricks (Agentic AI Solutions)

3 weeks ago


Bengaluru, India NTT Full time

JOB DESCRIPTION Req ID:     We are currently seeking a Architect - Google Vertex AI & Databricks (Agentic AI Solutions) to join our team in Bangalore, Karnātaka (IN-KA), India (IN). Job Duties: 1. Architecture & Solution Design• Design end-to-end agentic AI systems integrating Vertex AI Agent Builder, Gemini models, and Databricks Mosaic AI Agent Framework.• Develop hybrid architectures where Databricks is the governed data and tool plane, and Vertex AI is the reasoning/LLM runtime (and vice-versa as per client needs).• Define patterns for RAG, tool calling, workflow orchestration, and multi-agent systems.• Architect secure connectivity between Google Cloud and Databricks using IAM, service accounts, and workload identity federation.2. Databricks Lakehouse Expertise• Architect AI/analytics systems using Delta Lake, Unity Catalog, Databricks SQL, MLflow, and Databricks Vector Search.• Develop governed toolsets (SQL tools, Python functions, REST connectors) for agent use cases.• Optimize pipelines using Delta Live Tables, streaming architectures, and feature/embedding stores.3. Google Cloud / Vertex AI Expertise• Implement and optimize Gemini 1.5/2.0/2.5 Pro, Flash, and Nano models on Vertex AI.• Build production agents using Vertex AI Agent Builder with tool integrations, schema-based prompts, grounding, and observability.• Leverage Model Garden, Model Armor, Evaluation, and Realtime Inference APIs for secure and compliant deployment.4. Agentic & Generative AI Implementation• Build enterprise-grade agent-based systems with robust tool-use, grounding, error recovery, and structured outputs.• Reference architectures for:o LLM-powered copilotso Autonomous data/ops agentso Customer support/search agentso Supply-chain or financial intelligence agents• Integrate RAG using vector stores, embedding pipelines, and multimodal grounding.5. MLOps, DevOps & Governance• Define CI/CD for AI workloads using MLflow, GitHub/GitLab, Cloud Build, or Azure DevOps.• Implement observability dashboards for latency, tokens, tool calls, success rate, and hallucination metrics.• Apply data/AI governance policies, lineage, and security using Unity Catalog, Vertex Model Armor, GCP IAM, and secrets management.6. Stakeholder Management & Technical Leadership• Work with cross-functional teams (data engineering, cloud, infra, product) to deliver high-impact AI platforms.• Mentor teams on generative AI, Databricks Lakehouse, GCP, and modern agentic paradigms.• Translate business objectives into solution architectures and technical roadmaps.Minimum Skills Required: Required Skills & Qualifications• 10+ years of experience in Data Engineering / ML / Cloud Architecture.• Expertise in Databricks (Delta Lake, DLT, SQL, UC, MLflow, Mosaic AI).• Strong hands-on experience with Google Cloud (BigQuery, Vertex AI, Cloud Run, GCS, IAM).• Experience building agentic AI systems with Vertex AI or Databricks Mosaic tools.• Strong programming background in Python (LangChain/Agent frameworks optional but beneficial).• Expertise in RAG pipelines, prompting, schema-based prompting, and retrieval optimization.• Experience deploying and scaling AI solutions in production. About NTT DATA NTT DATA is a $30 billion business and technology services leader, serving 75% of the Fortune Global 100. We are committed to accelerating client success and positively impacting society through responsible innovation. We are one of the world's leading AI and digital infrastructure providers, with unmatched capabilities in enterprise-scale AI, cloud, security, connectivity, data centers and application services. our consulting and Industry solutions help organizations and society move confidently and sustainably into the digital future. As a Global Top Employer, we have experts in more than 50 countries. We also offer clients access to a robust ecosystem of innovation centers as well as established and start-up partners. NTT DATA is a part of NTT Group, which invests over $3 billion each year in R&D.



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