AI Architect - Google AI & Generative Intelligence

We are seeking a highly accomplished AI Architect with deep expertise in Google AI technologies and Generative AI to lead the design and implementation of enterprise-scale AI solutions. This role requires strong architectural vision, hands-on technical depth, and leadership in building production-grade AI systems leveraging LLMs, SLMs, and multi-agent frameworks . The ideal candidate will drive AI strategy, define scalable architectures, and lead cross-functional teams in delivering cutting-edge AI-powered applications using the Google Cloud ecosystem , modern AI frameworks, and robust MLOps practices.

Key Responsibilities:

AI Architecture & Strategy Define end-to-end AI/GenAI architecture for enterprise-grade applications. Establish best practices for LLM/SLM adoption, multi-agent systems, and RAG architectures . Drive AI platform strategy leveraging Google Cloud (Vertex AI, GKE, Cloud Run) . Lead architecture reviews, technical governance, and design standards.

LLM / SLM

& Generative AI Solutions Architect solutions using commercial LLMs such as Gemini, GPT, and Claude . Design scalable systems using open-source models (Mixtral, Mistral, Gemma, Phi-3) . Define strategies for fine-tuning (LoRA, QLoRA, PEFT) and model optimization. Oversee model evaluation frameworks and benchmarking (HELM, lm-eval, RAGAS). Google AI Ecosystem Leadership Lead adoption of: Vertex AI for model lifecycle management Google Agent Development Kit (ADK) for intelligent agents Google Workspace integrations (Docs, Sheets, Gmail, Drive, Meet) Architect solutions using BigQuery, Lakehouse, and Vector Databases . AI Platform & MLOps Architecture Design scalable MLOps pipelines for training, deployment, and monitoring. Define CI/CD strategies for AI systems using GitHub Actions / GitLab CI . Establish observability frameworks using LangSmith, MLflow, Weights & Biases . Optimize infrastructure cost and performance across cloud and hybrid environments. Multi-Agent Systems & AI Frameworks Architect complex workflows using: LangChain, LlamaIndex, LangGraph Semantic Kernel for multi-agent orchestration Design intelligent automation pipelines and agent collaboration patterns. Data & RAG Architecture Design enterprise RAG pipelines using Vertex AI Vector DB, ChromaDB . Define data ingestion, transformation, and governance strategies. Architect semantic search and knowledge retrieval systems. Application & Integration Architecture Define backend architecture using FastAPI / Node.js APIs . Architect API management and security using Apigee / MuleSoft . Guide frontend architecture using React / Angular for AI-driven applications. Engineering Leadership Provide technical leadership and mentorship to AI/ML engineers. Collaborate with product, data, and engineering teams for solution delivery. Lead design documentation, architecture diagrams, and technical roadmaps. Ensure adherence to coding standards, testing, and quality frameworks. Deployment & Infrastructure Architect deployments across: Google Cloud Platform (Vertex AI, GKE, Cloud Run) Hybrid and on-prem environments Edge AI use cases Ensure scalability, reliability, and security of AI systems. AI Governance & Responsible AI Define frameworks for AI ethics, bias mitigation, and explainability . Establish governance for model lifecycle, monitoring, and compliance . Implement safeguards for hallucination detection and output validation.

Required Qualifications:

12 18 years of software engineering experience. 7+ years in AI/ML with strong focus on Generative AI and LLMs . Deep expertise in Google AI ecosystem (Vertex AI, Gemini, ADK, AI Studio) . Strong experience in LLMs, SLMs, RAG, and multi-agent architectures . Proficiency in Python and familiarity with Node.js . Hands-on experience with MLOps, CI/CD, and cloud-native architecture (Google Cloud Platform) . Proven experience designing scalable, production-grade AI systems .

Preferred Qualifications:

Google Cloud Certifications ( Professional ML Engineer / Cloud Architect ). Experience contributing to open-source AI/ML projects . Expertise in edge AI and hybrid cloud deployments . Experience building enterprise AI platforms or COEs . Strong leadership experience mentoring and scaling AI teams.

Key Skills Summary:

Generative AI (LLMs, SLMs, RAG, Agents) Google Cloud AI Stack (Vertex AI, Gemini, ADK) AI Frameworks (LangChain, LangGraph, LlamaIndex, Semantic Kernel) MLOps & Observability (MLflow, W&B, LangSmith) Cloud & Infrastructure (Google Cloud Platform, Kubernetes, Serverless) Backend & APIs (FastAPI, Node.js, Apigee) Data & Vector DBs (BigQuery, ChromaDB, Vector Search)