AI Architect

Project description

We are seeking an experienced and visionary Generative AI Architect to lead the design, architecture, and adoption of enterprise-scale Generative AI solutions. This role will be responsible for defining AI strategy, architecting scalable and secure GenAI platforms, and enabling the successful implementation of AI-powered products across the organization. You should possess deep expertise in Large Language Models (LLMs), Agentic AI, Retrieval-Augmented Generation (RAG), MLOps, cloud-native architectures, and enterprise integration patterns. The architect will work closely with business stakeholders, engineering teams, data scientists, and technology leadership to deliver transformative AI solutions that drive business value.

Responsibilities

• AI Strategy & Architecture: Define and drive the enterprise Generative AI architecture vision, standards, and technology roadmap. Design scalable, resilient, secure, and cost-optimized AI solutions leveraging LLMs, Agentic AI frameworks, multimodal models, and AI platforms. Establish reference architectures, reusable frameworks, and best practices for enterprise AI adoption. Lead architecture reviews and provide technical governance across AI initiatives. Evaluate emerging AI technologies, models, frameworks, and vendors to guide strategic investments. Generative AI Solution Design: Architect and oversee the implementation of enterprise-grade GenAI applications, including: Intelligent Assistants and Chatbots Enterprise Search and Knowledge Management Document Intelligence and Summarization Code Generation and Developer Productivity Solutions Agentic AI and Autonomous Workflows Multimodal AI Applications: Design advanced RAG architectures using vector databases, knowledge graphs, semantic search, and hybrid retrieval techniques. Architect prompt engineering frameworks, agent orchestration patterns, memory mechanisms, and contextual reasoning pipelines. Platform & Cloud Architecture: Design cloud-native AI platforms on AWS, Azure, and GCP. Define AI infrastructure requirements, including GPU utilization, model serving, inference optimization, and scaling strategies. Architect enterprise AI workbenches and reusable platform capabilities. Design secure API and microservices-based architectures for GenAI integration. Enable hybrid and multi-cloud AI deployment strategies. AI Engineering & MLOps: Define enterprise MLOps and LLMOps frameworks for model lifecycle management. Architect CI/CD pipelines for AI model training, validation, deployment, monitoring, and governance. Lead implementation of AI observability, model monitoring, drift detection, performance tracking, and operational excellence. Guide optimization strategies, including model quantization, distillation, caching, and inference acceleration. Security, Compliance & Responsible AI Establish AI governance frameworks aligned with organizational and regulatory requirements. Define security controls for GenAI systems, including prompt injection protection, data privacy, model security, and access management. Ensure compliance with Responsible AI principles, security regulations, and enterprise governance standards. Conduct architecture risk assessments and recommend mitigation strategies. Stakeholder Leadership & Innovation: Partner with business executives and domain leaders to identify high-value AI transformation opportunities. Translate business requirements into scalable AI architecture solutions. Lead discovery workshops, architecture assessments, and client presentations. Mentor AI engineers, solution architects, and development teams. Drive innovation through PoCs, accelerators, reusable assets, and AI platform components. Documentation & Governance: Develop architecture blueprints, design standards, technical roadmaps, and implementation guidelines. Present architecture recommendations, business cases, and technical strategies to leadership and executive stakeholders. Support enterprise-wide AI adoption and change management initiatives.

SKILLS

Must have

• 12+ years of overall IT experience with 6+ years in AI/ML and 4+ years leading Generative AI architecture and solution design initiatives. Proven experience architecting large-scale enterprise AI and GenAI platforms from concept through production deployment. Experience leading technical teams and cross-functional enterprise programs. Generative AI Expertise Deep understanding of: Large Language Models (GPT, Claude, Gemini, LLaMA, Mistral) Transformer architectures Diffusion models Multimodal AI systems Agentic AI frameworks Strong experience with: Prompt Engineering RAG architectures Fine-tuning and model customization Embedding models AI orchestration frameworks Technology Stack Expert-level proficiency in Python. Strong experience with Java and/or Node.js Hands-on experience with: LangChain LangGraph LlamaIndex Hugging Face Semantic Kernel AutoGen or similar agent frameworks Data & Retrieval Technologies Experience designing enterprise data architectures supporting AI workloads Expertise with vector databases, including: Pinecone Weaviate Chroma FAISS Azure AI Search Strong understanding of ETL, metadata management, knowledge management, and data pipelines. Cloud & Platform Engineering Strong expertise in one or more cloud platforms: Microsoft Azure AWS Google Cloud Platform Hands-on experience with: Azure OpenAI Vertex AI Amazon Bedrock Azure AI Foundry Azure ML MLflow Kubeflow Architecture & Enterprise Skills Strong expertise in: Distributed systems architecture Microservices API design Event-driven architectures Enterprise integration patterns Security and governance frameworks Experience defining architecture standards and technology roadmaps. Leadership & Communication: Excellent stakeholder management and executive communication skills Ability to influence technical and business leaders Strong consulting, presentation, and problem-solving abilities Experience mentoring architects, engineers, and AI practitioners

Nice to have

N/A