ML​/AI Architect

Job Description

DXC Technology is a Fortune 500 Global IT Services Leader and is ranked  more than 130,000 people in 70-plus countries are entrusted by our customers to deliver what matters most. We use the power of technology to deliver mission critical IT services that transform global businesses. We deliver excellence for our customers, colleagues and communities around the world.

Accelerate your career and reimagine the possibilities with DXC!

We inspire and take care of our people. Work in a culture that encourages innovation and where brilliant people embrace change and seize opportunities to advance their careers and amplify customer success. Leverage technology skills and deep industry knowledge to help clients. Work on transformation programs that modernize operations and drive innovation across our customer’s entire IT estate using the latest technologies in cloud, applications, security, IT Outsourcing, business process outsourcing and modern workplace.

We are seeking an experienced AI Architect, based in London to lead the design and implementation of cutting‑edge AI solutions, ensuring they are safe, reliable, and high‑performing.

• AI Strategy & Leadership: Lead the end-to-end development of advanced AI models and frameworks (including large language models, transformers, and agent-based systems), aligning AI initiatives with business goals. Provide technical leadership to data science and engineering teams, and set strategic direction for AI projects.
• Architectural Design: Design and oversee the integration of AI components such as LLMs, transformer architectures, retrieval‑augmented generation (RAG) workflows, and vector databases into scalable solutions. Establish standards for model architecture and pipeline optimisation to ensure robustness and efficiency.
• Evaluation & Safety: Define and enforce rigorous evaluation protocols for AI models, including performance metrics, validation techniques, and safety checks. Ensure all AI systems meet reliability standards, ethical guidelines, and regulatory requirements for responsible AI usage.
• MLOps & Deployment: Guide the development of MLOps pipelines for continuous training, testing, and deployment of models in cloud environments. Oversee infrastructure decisions (without bias to specific platforms) to guarantee that AI services are scalable, secure, and maintainable in production.

• Educational Background: Master’s or PhD in Computer Science, Artificial Intelligence, Machine Learning or a related field. Strong theoretical foundation in machine learning, deep learning, and AI system design.
• Technical Expertise: Expert‑level proficiency in programming (e.g. Python) and ML frameworks (such as Tensor Flow or PyTorch). In-depth knowledge of modern AI techniques including natural language processing, transformer networks, reinforcement learning, and data engineering for AI (ETL, feature engineering).
• AI/ML Tools: Extensive experience with AI and MLOps tool chains – from model development and version control to continuous integration and automated deployment. Familiarity with vector stores/databases, orchestration of agent frameworks, and evaluation libraries/methodologies for model performance.
• Analytical
  
  Skills:
  
  Exceptional problem‑solving abilities and mathematical skills (linear algebra, calculus, statistics) to innovate and troubleshoot complex AI modelling challenges.

• LLMs & Transformers: Proven track record of developing, fine‑tuning, or deploying large‑scale language models and transformer‑based architectures in real‑world applications.
• RAG & Vector Databases: Hands‑on experience implementing retrieval‑augmented generation techniques and using vector databases (embedding stores) to integrate external knowledge or context into AI models.
• Agent Systems: Background in building or utilising intelligent agents or multi‑agent systems for automated decision‑making, with an understanding of how to evaluate and optimise their performance.
• AI at Scale: Experience deploying AI solutions in cloud or distributed environments at scale (industry…