MLAI Architect (London based)

Job Description:

DXC Technology is a Fortune 500 Global IT Services Leader and is ranked at 152. Our more than 130000 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 customers entire IT estate using the latest technologies in cloud applications security IT Outsourcing business process outsourcing and modern workplace.

Senior ML/AI Architect 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.

Key Responsibilities:

• 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.

Required Qualifications:

• Educational Background: Masters 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 TensorFlow 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 toolchains 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.

Desired Experience:

• 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 agnostic e.g. handling large datasets or high-throughput systems). Comfortable optimising models for production monitoring their performance and iterating based on results.
• Stakeholder Collaboration: History of working closely with cross-functional teams (product DevOps data engineering research) to drive AI projects from concept to completion including gathering requirements and managing feedback cycles.

At DXC Technology we believe strong connections and community are key to our success. Our work model prioritizes in-person collaboration while offering flexibility to support wellbeing productivity individual work styles and life circumstances. Were committed to fostering an inclusive environment where everyone can thrive.

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