Enterprice AI Architect

Job Title: Enterprise AI Architect
Location: Remote
Employment Type: Contract / Full-time (as applicable)
Work Arrangement: 100% Remote

Job Summary

We are seeking an experienced Enterprise AI Architect to design and lead enterprise-scale Artificial Intelligence (AI) solutions aligned with business objectives and technology strategy. The ideal candidate will have extensive experience in AI/ML architecture, Generative AI, Large Language Models (LLMs), cloud platforms, enterprise data architecture, and responsible AI practices.

The Enterprise AI Architect will collaborate with business stakeholders, data scientists, ML engineers, software engineers, and cloud teams to develop secure, scalable, and production-ready AI solutions. This role will drive AI adoption, define enterprise AI architecture standards, and ensure AI solutions integrate effectively with existing enterprise systems.

Key Responsibilities

• Define and implement enterprise AI architecture, technology roadmaps, and AI adoption strategies.

• Design scalable AI/ML and Generative AI solutions for enterprise business use cases.

• Architect LLM-based applications using commercial and open-source models.

• Design Retrieval-Augmented Generation (RAG) solutions, vector search, semantic search, and enterprise knowledge assistants.

• Evaluate AI models, frameworks, platforms, and tools based on business requirements, performance, scalability, and cost.

• Design and implement AI integration patterns using APIs, microservices, and enterprise application architectures.

• Develop architecture standards for model development, deployment, monitoring, and lifecycle management.

• Establish MLOps and LLMOps practices for automated model deployment, versioning, monitoring, and governance.

• Lead the integration of AI capabilities into existing enterprise applications, workflows, and business processes.

• Design secure AI architectures incorporating identity management, data privacy, access controls, encryption, and secrets management.

• Implement responsible AI practices covering model explainability, bias mitigation, data protection, and governance.

• Collaborate with data engineering teams to establish reliable data pipelines and data access patterns for AI workloads.

• Define AI architecture reference models, design patterns, technical standards, and reusable components.

• Assess AI solution performance, latency, scalability, reliability, and total cost of ownership.

• Communicate architectural decisions, technical recommendations, and roadmaps to executive stakeholders.

• Mentor engineering teams and provide technical leadership throughout the AI solution lifecycle.

Required Qualifications

• Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field; a master's degree is preferred.

• 10+ years of experience in enterprise architecture, software architecture, data architecture, AI/ML engineering, or related technology roles.

• 5+ years of experience designing or implementing AI, machine learning, data, or cloud-based solutions, with substantial hands-on AI architecture experience.

• Proven experience designing enterprise-scale AI/ML solutions and integrating them into production environments.

• Strong understanding of machine learning, deep learning, NLP, model evaluation, and AI solution design.

• Hands-on experience with Generative AI, LLMs, prompt engineering, and RAG architectures.

• Experience with AI frameworks and libraries such as LangChain, LlamaIndex, Hugging Face, PyTorch, or TensorFlow.

• Strong programming and API integration knowledge, particularly Python and REST APIs.

• Experience with at least one major cloud platform: AWS, Microsoft Azure, or Google Cloud Platform.

• Familiarity with vector databases and search technologies such as Pinecone, Weaviate, Milvus, FAISS, or Azure AI Search.

• Experience with data platforms, enterprise data integration, SQL/NoSQL databases, and data processing pipelines.

• Knowledge of containerization, microservices, Kubernetes, CI/CD, and production deployment practices.

• Understanding of AI security, data governance, model monitoring, responsible AI, and regulatory considerations.

• Excellent communication, architectural documentation, stakeholder management, and technical leadership skills.

Preferred Qualifications

• Experience with enterprise AI platforms such as Azure AI Foundry, Amazon Bedrock, Google Vertex AI, or equivalent services.

• Experience implementing AI agents, tool calling, multi-agent workflows, and orchestration frameworks.

• Knowledge of Model Context Protocol (MCP) and enterprise tool integration patterns.

• Experience with MLOps/LLMOps tools such as MLflow, Kubeflow, or cloud-native model lifecycle services.

• Familiarity with knowledge graphs, graph databases, and enterprise semantic layers.

• Experience with AI evaluation frameworks, guardrails, observability, and prompt/model version management.

• Knowledge of enterprise architecture frameworks such as TOGAF or equivalent.

• Relevant cloud, AI, or enterprise architecture certifications.

Technical Skills

• AI/ML: Machine Learning, Deep Learning, NLP, Generative AI

• LLMs: OpenAI models, Anthropic Claude, Llama, and other enterprise LLMs

• Generative AI: RAG, embeddings, prompt engineering, AI agents, guardrails

• AI Frameworks: LangChain, LlamaIndex, Hugging Face, PyTorch, TensorFlow

• Cloud AI Platforms: AWS Bedrock, Azure AI Foundry, Google Vertex AI

• Vector Search: Pinecone, Weaviate, FAISS, Milvus, Azure AI Search

• Programming: Python, SQL, REST APIs

• Data Engineering: ETL/ELT, data pipelines, data lakes, data warehouses

• MLOps/LLMOps: MLflow, Kubeflow, model deployment and monitoring

• Architecture: Microservices, APIs, distributed systems, enterprise integration

• Security and Governance: IAM, encryption, responsible AI, data privacy, AI risk management

Key Competencies

• Enterprise AI Strategy and Architecture

• Generative AI and LLM Solution Design

• AI Platform and Cloud Architecture

• RAG and Enterprise Knowledge Systems

• AI Agent Architecture and Orchestration

• AI Governance, Security, and Responsible AI

• MLOps and LLMOps

• Enterprise Integration and Data Architecture

• Technical Leadership and Stakeholder Management

• Architecture Roadmaps and Technology Evaluation

Expected Deliverables

• Enterprise AI reference architecture and technology roadmap.

• Scalable, secure, production-ready AI solution designs.

• Reusable AI components, integration patterns, and architecture standards.

• AI governance, security, and operational guidelines.

• Recommendations for AI platform selection and cost optimization.

• Production deployment, monitoring, and lifecycle management strategy.

Work Location

Fully remote. Candidates must be able to collaborate with distributed teams and attend virtual meetings during the client's required business hours.