Senior AI Solution Architect

About the Role

This position focuses on designing and implementing advanced AI solutions on Google Cloud Platform (GCP), combining deep technical expertise with emerging Agentic AI requirements. The ideal candidate will bridge business objectives and technical execution, leading high-impact AI initiatives.

Key Skill Clusters

• Agentic AI & Orchestration Frameworks

  Build systems where AI agents autonomously execute multi-step workflows using APIs (tools), moving beyond basic chatbots.

  Keywords: Vertex AI Agent Builder, LangChain, LangGraph, CrewAI, AutoGen.

• Vertex AI & MLOps Lifecycle

  Expertise in productionizing models on GCP, including model drift detection, automated retraining pipelines, and CI/CD for ML.

  Keywords: Vertex AI Pipelines, Model Registry, Feature Store, Model Monitoring, MLOps.

• GCP Data Lakehouse Architecture

  Design and implement unified data architectures combining data lakes (unstructured storage) and data warehouses (structured SQL) on GCP.

  Keywords: BigQuery (BigQuery ML, BigLake), Dataproc, Dataflow, Medallion Architecture (Bronze/Silver/Gold).

• Generative AI & RAG (Retrieval-Augmented Generation)

  Architect solutions using LLMs like Gemini, grounding models in company-specific data to prevent hallucinations and ensure accuracy.

  Keywords: Gemini (Pro/Flash), Vertex AI Search & Conversation, Vector Databases, Prompt Engineering, Embeddings.

• Cross-Functional Technical Leadership

  Serve as a senior visionary, translating business ROI into technical solutions, mentoring teams, and evaluating GenAI tools/vendors.

  Keywords: Reference Architectures, Stakeholder Management, Solution Blueprints, Cost Optimization (FinOps).

Responsibilities

• Design and deploy Agentic AI systems with autonomous, multi-step workflows using tools like Vertex AI Agent Builder, LangChain, and AutoGen.
• Productionize ML models on GCP, implementing MLOps practices such as model drift detection, automated retraining, and CI/CD pipelines.
• Architect GCP-based data lakehouse solutions, unifying structured and unstructured data storage (e.g., BigQuery, BigLake, Medallion Architecture).
• Develop Generative AI solutions using LLMs (e.g., Gemini) and RAG architectures to ensure accurate, data-grounded outputs.
• Lead cross-functional initiatives, presenting technical solutions to CXOs, mentoring teams, and evaluating GenAI tools for business impact.
• Optimize AI solutions for cost, performance, and scalability, aligning technical implementation with business objectives.