AI Architect / Generative AI Architect

Job description

**About the Role** We are looking for an experienced **AI Architect** to lead the design, development, and deployment of enterprise-scale Artificial Intelligence solutions. The ideal candidate will have strong expertise in **Generative AI, Large Language Models (LLMs), Agentic AI, RAG architectures, Machine Learning, Cloud Platforms, and Enterprise Solution Architecture**. You will work closely with business stakeholders, data scientists, engineering teams, and product leadership to build scalable AI platforms and transform business processes through AI-driven innovation. **Key Responsibilities** - Design and architect end-to-end AI/GenAI solutions aligned with business objectives. - Define enterprise AI strategy, architecture standards, and best practices. - Build and implement Generative AI applications leveraging LLMs, RAG, Agents, and multi-agent frameworks. - Design scalable AI platforms using Azure OpenAI, AWS Bedrock, Google Vertex AI, or equivalent cloud services. - Collaborate with cross-functional teams to translate business requirements into technical AI solutions. - Evaluate and recommend AI frameworks, models, tools, and infrastructure. - Define data, model, and deployment architectures ensuring scalability, security, and governance. - Lead AI solution design reviews and provide technical leadership during implementation. - Ensure AI solutions comply with security, privacy, and responsible AI principles. - Drive MLOps/LLMOps practices for model deployment, monitoring, observability, and lifecycle management. - Mentor engineering and data science teams on AI architecture and best practice **Required Skills & Qualifications** **AI & Machine Learning** - Strong experience in Machine Learning and Deep Learning concepts. - Hands-on expertise with Large Language Models (LLMs). - Experience with Generative AI solution development. - Knowledge of Retrieval-Augmented Generation (RAG). - Experience building Agentic AI and autonomous AI workflows. - Prompt Engineering and AI model optimization. **Technical Skills** - Strong programming expertise in Python. - Experience with PyTorch, TensorFlow, Scikit-Learn. - Hands-on experience with LangChain, LlamaIndex, Semantic Kernel, CrewAI, or AutoGen. - API integration and microservices architecture experience. **Cloud & Infrastructure** - Experience with Azure OpenAI, Azure AI Services, AWS Bedrock, Amazon SageMaker, or Google Vertex AI. - Knowledge of Docker and Kubernetes. - Experience designing cloud-native AI solutions. **Data & Platforms** - Experience with SQL and NoSQL databases. - Experience with Vector Databases such as Pinecone, Weaviate, ChromaDB, or Milvus. - Strong understanding of data architecture and data engineering principles. **Architecture & Governance** - Enterprise Architecture and Solution Design expertise. - AI Governance, Responsible AI, Model Monitoring, and Compliance frameworks. - Strong understanding of scalability, reliability, and security architecture.