About the role
Title: AI Architect
We are seeking a highly experienced AI Architect to lead the design, architecture, and implementation of enterprise-scale Generative AI solutions on Google Cloud Platform (GCP). The ideal candidate will possess deep expertise in Retrieval-Augmented Generation (RAG), Large Language Models (LLMs), Agentic AI, Vector Databases, and the Google AI ecosystem including Vertex AI and Gemini. The role requires a blend of strategic architecture leadership and hands-on technical expertise to deliver scalable, secure, and production-ready AI platforms.
Years of Experience:
Required:
Key Responsibilities:
AI Architecture & Strategy
We are seeking a highly experienced AI Architect to lead the design, architecture, and implementation of enterprise-scale Generative AI solutions on Google Cloud Platform (GCP). The ideal candidate will possess deep expertise in Retrieval-Augmented Generation (RAG), Large Language Models (LLMs), Agentic AI, Vector Databases, and the Google AI ecosystem including Vertex AI and Gemini. The role requires a blend of strategic architecture leadership and hands-on technical expertise to deliver scalable, secure, and production-ready AI platforms.
Years of Experience:
- 10 years of overall experience in software engineering, cloud architecture, or data platforms.
- 5 years of experience designing and implementing AI/ML solutions.
- 3 years of experience delivering Generative AI and LLM-based applications in enterprise environments.
- Proven experience implementing RAG architectures, conversational AI platforms, and AI-powered knowledge management solutions.
Required:
- Bachelor s degree in Computer Science, Data Science.
- Generative AI & LLMs: Gemini, Vertex AI, OpenAI, Llama, Foundation Models, Prompt Engineering, Conversational AI, Agentic AI / Multi-Agent Architectures, AI Model Evaluation and Monitoring
- RAG & Knowledge Systems: Retrieval-Augmented Generation (RAG), GraphRAG, Knowledge Graphs, Semantic Search, Embeddings and Vector Search, Document Intelligence and Enterprise Search
- Google Cloud Platform (GCP): Vertex AI, BigQuery, Cloud Storage, Cloud Run, Cloud Functions, Cloud SQL, Pub/Sub, Dataflow, GKE (Google Kubernetes Engine)
- Vector Databases & Search: Vertex AI Vector Search, Pinecone, ChromaDB, FAISS, pgVector
- AI Frameworks & Development: LangChain, LangGraph, LlamaIndex, FastAPI, REST APIs, Python, SQL
- MLOps & DevOps: Vertex AI Pipelines, MLflow, CI/CD for AI Applications, Model Governance & Monitoring, Infrastructure as Code (Terraform)
- Security & Governance: Responsible AI, AI Risk Management, Data Governance, Security Architecture, Compliance & Audit Controls
- Google Cloud Professional Cloud Architect
- Google Cloud Professional Machine Learning Engineer
- Google Generative AI Certifications
- Databricks Generative AI Certifications (preferred)
Key Responsibilities:
AI Architecture & Strategy
- Define enterprise AI architecture standards, patterns, and best practices.
- Design end-to-end Generative AI, RAG, and Agentic AI solutions.
- Develop AI roadmaps aligned with business objectives and technology strategy.
- Architect large-scale RAG and GraphRAG solutions.
- Design document ingestion, chunking, indexing, retrieval, re-ranking, and grounding strategies.
- Optimize AI solution accuracy, scalability, latency, and cost efficiency.
- Build enterprise knowledge platforms leveraging structured and unstructured data sources.
- Lead the design and implementation of AI assistants, chatbots, copilots, and autonomous agents.
- Enable integration of LLMs with enterprise systems, APIs, and workflows.
- Establish frameworks for prompt engineering, model evaluation, and continuous improvement.
- Architect scalable AI platforms using GCP services.
- Drive cloud-native AI application development and deployment.
- Define best practices for performance optimization, reliability, observability, and resilience.
- Implement AI governance frameworks, security controls, and monitoring capabilities.
- Ensure compliance with enterprise policies, data privacy, and regulatory requirements.
- Establish standards for model transparency, explainability, and risk management.
- Partner with business stakeholders, product owners, data engineers, and AI teams.
- Conduct architecture reviews and technical design workshops.
- Mentor engineering teams and promote AI adoption across the organization.
- Present architecture recommendations and investment strategies to executive leadership.