Enterprise AI Architect

The Enterprise AI Architect is responsible for designing, implementing, and overseeing enterprise-grade AI/ML platforms and solutions that drive innovation, scalability, and business value. This role blends deep technical expertise with strategic vision and leadership to architect robust, secure, and ethical AI ecosystems aligned with organizational goals.

Acting as a bridge between business strategy and technical execution, the Enterprise AI Architect enables and advances the organization's AI capabilities by designing cloud-native infrastructure, automating workflows, and operationalizing AI/ML models using modern DevOps/MLOps practices. The role ensures seamless integration, monitoring, and management of AI services across production environments.

Collaboration is central to this role — working closely with executives, AI engineers, platform architects, data scientists, and product teams to deliver end-to-end AI solutions. From data pipelines to deployment, the architect ensures that systems are scalable, compliant, and performance-optimized, while championing responsible AI practices throughout the lifecycle.

Salary Information

Pay is based on relevant experience, skills for the role, and location. Salary is only one part of our total reward package.

Location: Guildford or London 

About You

Strategy & Platform Architecture

• Define and drive the organization's AI/ML architecture strategy, frameworks, and best practices.
• Develop strategic roadmaps for AI initiatives aligned with business objectives and future technology direction.
• Evaluate emerging AI/ML technologies, platforms, and cloud services to recommend adoption where appropriate.

End-to-End Solution Design

• Architect scalable, secure, and reliable AI/ML systems — from data ingestion and feature engineering to model training, deployment, and monitoring.
• Design and operationalize cloud-native AI capabilities using modern DevOps/MLOps practices.
• Ensure alignment with data governance, compliance, and ethical AI standards.

 Infrastructure & Integration

• Oversee the design and provisioning of infrastructure to support AI/ML workloads.
• Integrate AI systems with existing enterprise infrastructure, applications, and databases to maximize business value and operational efficiency.
• Monitor and optimize performance, scalability, and cost-effectiveness of AI platforms.

Governance & Lifecycle Management

• Establish governance frameworks for model management, monitoring, retraining, and risk mitigation.
• Implement MLOps workflows to streamline deployment, versioning, and lifecycle management of AI models.
• Collaborate with security teams to ensure compliance and manage platform risks.

Collaboration & Technical Leadership

• Partner with business stakeholders to identify opportunities for AI-driven transformation.
• Provide technical leadership and mentorship to AI engineers, data scientists, and development teams.
• Communicate complex AI concepts and strategies in clear, business-relevant terms.

Essential Skills

AI/ML Expertise

• Deep knowledge of machine learning, deep learning, natural language processing, and emerging AI paradigms including Generative AI, Agentic AI, and Graph-based modeling.
• Hands-on experience designing, building, and scaling AI solutions using major frameworks such as TensorFlow, PyTorch, LangChain, and Semantic Kernel.
  
  

Cloud & Infrastructure Proficiency

• Proven ability to architect and deploy AI/ML solutions on leading cloud platforms: AWS, Azure, and Google Cloud Platform, leveraging native services for scalability, security, and performance.
• Expertise in containerized environments using Docker and Kubernetes, with excellent understanding of cloud-native design principles.
  
  

Data Engineering & Pipeline Design

• Excellent command of data pipeline design and big data technologies including Apache Spark, Kafka, and Airflow.
• Experience in managing feature stores, training data pipelines, and real-time data ingestion for AI/ML workflows.
  
  

MLOps & DevOps Practices

• Proficiency in implementing MLOps and CI/CD pipelines for model versioning, automated deployment, monitoring, and lifecycle management.
• Familiarity with tools and platforms such as Databricks, AI Foundry, and Amazon Bedrock for operationalizing AI models.
  
  

Programming & Software Engineering

• Advanced programming skills in Python, with solid understanding of software engineering principles for building production-grade AI systems.
• Experience in developing modular, testable, and maintainable codebases for AI/ML applications.
  
  

System Integration & Architecture

• Ability to define and implement end-to-end AI/ML architectures that integrate seamlessly with enterprise IT systems, applications, and databases.
• Skilled in designing robust, scalable, and interoperable AI platforms that support business-critical use cases.
  
  

Governance, Security & Responsible AI

• Sound understanding of data privacy, compliance, and ethical AI standards.
• Experience in establishing governance frameworks and collaborating with security teams to manage risks and ensure regulatory alignment.
  
  

Experience

• Extensive experience in software development, data science, ML engineering, or AI platform architecture, with an extensive portfolio of successful, enterprise-scale AI/ML projects.
• Demonstrated expertise in designing and implementing end-to-end AI pipelines, transitioning models from experimentation to production with reliability and efficiency.
• Led the architectural strategy for AI initiatives, aligning technical execution with business goals, security requirements, and existing infrastructure.
• Proven experience in complex AI platform design, integrating diverse technologies (open-source and commercial) into scalable, cost-effective, and secure AI ecosystems.
• Designed and deployed AI/ML solutions across multi-cloud environments (AWS, Azure, GCP), leveraging cloud-native services for performance optimization and cost management.
• Deep understanding of distributed systems, with a focus on scalability, fault-tolerance, and performance tuning in production environments.
• Extensive background in MLOps and DevOps, including CI/CD pipelines, model versioning, containerization (Docker), orchestration (Kubernetes), and monitoring.
• Extensive experience in data engineering, including big data technologies (Spark, Kafka), feature stores, and robust data management strategies.
• Hands-on expertise across the machine learning lifecycle, including model selection, evaluation, interpretability (e.g., SHAP, LIME), retraining, and monitoring.
• Experience in strategic AI roadmap development, gaining stakeholder buy-in and aligning AI initiatives with long-term business objectives.
• Successfully led AI transformation projects in enterprise environments, integrating AI/ML systems with legacy applications, ERP/CRM platforms, IoT data sources, and hybrid cloud setups.
• Established and enforced AI governance frameworks, ensuring ethical standards, data privacy, and regulatory compliance.
• Experience deploying large language models (LLMs) and generative AI systems, with familiarity in vector databases, retrieval-augmented generation (RAG), and knowledge graphs.
• Excellent communication and stakeholder management skills, with the ability to translate complex AI concepts into clear business value.

We support the government scheme "Access to Work" please feel free to ask us about the access to work scheme.

Desirable Skills

• A bachelor's or master's degree in computer science, data science, or a related technical field