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., 1. 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.

1. 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).

2. 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.

3. 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.

4. 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.

5. 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.

6. 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.

7. 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.

8. Deployment & Infrastructure * Architect deployments across: + GCP (Vertex AI, GKE, Cloud Run) + Hybrid and on-prem environments + Edge AI use cases * Ensure scalability, reliability, and security of AI systems.

9. 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.

Do you have experience in Software engineering?, AI Architect Google AI & Generative Intelligence Experience Required: 12 18 Years in Software Engineering | 7+ Years in AI/ML & Generative AI, * 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 (GCP).
• Proven experience designing scalable, production-grade AI systems., * 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., * 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 (GCP, Kubernetes, Serverless)
• Backend & APIs (FastAPI, Node.js, Apigee)
• Data & Vector DBs (BigQuery, ChromaDB, Vector Search)