Senior AI Solution Architect(Databricks Eco System)

About the Role

This position focuses on designing and implementing advanced AI solutions on Google Cloud Platform (GCP), with a strong emphasis on Agentic AI and autonomous systems. The ideal candidate will bridge technical expertise with business impact, leading cross-functional initiatives to productionize AI models and architectures.

Key Responsibilities

• Design and build multi-step AI workflows using Agentic frameworks (e.g., Vertex AI Agent Builder, LangChain, LangGraph, CrewAI, AutoGen) where agents autonomously complete tasks via API integrations.
• Architect and productionize generative AI solutions using Vertex AI, including MLOps pipelines for model deployment, monitoring, and automated retraining (e.g., Model Drift detection, CI/CD for ML).
• Develop GCP-based data lakehouse architectures unifying structured and unstructured data (e.g., BigQuery ML, BigLake, Dataproc, Dataflow) with Medallion Architecture (Bronze/Silver/Gold).
• Implement Retrieval-Augmented Generation (RAG) systems using Gemini (Pro/Flash) and Vertex AI Search & Conversation, grounding LLMs in real-time, company-specific data to minimize hallucinations.
• Create reference architectures, solution blueprints, and cost-optimized (FinOps) AI implementations for enterprise-scale adoption.
• Lead technical evaluations of GenAI tools/vendors and mentor data engineering teams.
• Collaborate with stakeholders (including CXOs) to align AI solutions with business ROI and strategic goals.

Requirements

• 10–15+ years of experience in AI/ML and cloud architecture, with deep expertise in GCP’s Vertex AI suite (Pipelines, Model Registry, Feature Store, Model Monitoring).
• Proven track record building Agentic AI systems with autonomous, multi-step workflows (e.g., tool/API usage, decision-making agents).
• Hands-on experience with generative AI (Gemini, prompt engineering, embeddings) and RAG architectures (vector databases, real-time data retrieval).
• Strong background in GCP data lakehouse design (BigQuery ML, BigLake, Dataproc, Dataflow) and unifying data lakes/warehouses.
• Experience presenting technical solutions to executive leadership and driving cross-functional alignment.
• Familiarity with MLOps best practices, including model drift detection, automated retraining, and CI/CD for ML.

Skills

• Agentic AI & Orchestration: Vertex AI Agent Builder, LangChain, LangGraph, CrewAI, AutoGen.
• Vertex AI & MLOps: Vertex AI Pipelines, Model Registry, Feature Store, Model Monitoring, CI/CD for ML.
• GCP Data Lakehouse: BigQuery (ML, BigLake), Dataproc, Dataflow, Medallion Architecture.
• Generative AI & RAG: Gemini (Pro/Flash), Vertex AI Search & Conversation, prompt engineering, embeddings, vector databases.
• Technical Leadership: Reference architectures, stakeholder management, solution blueprints, FinOps, vendor/tool evaluations.