Enterprise AI/ML Scientist — GenAI & MLOps, London

Archer; Greater London; On-site; GBP 70,000 - 110,000; Full time

Job summary

Accenture is seeking an AI Engineer to design, build, and operationalize AI/ML solutions for enterprise clients in London. You will combine custom models with cloud services to deliver production-ready outcomes, addressing client needs from data assessment to deployment, with a focus on scalable data pipelines and DevOps for ML.

The role spans model selection, customization of Deep Learning and Gen AI, and collaboration with cross-functional teams to ensure value and reliability of deployed

Qualifications

• ML engineer or scientist with production deployment experience and monitoring.
• Strong CS foundations in architecture, systems, and programming.
• Experience with distributed computing and cloud environments.
• Experience building and deploying AI/ML software to cloud platforms.

Responsibilities

• Formulate real-world problems into scalable AI/ML solutions.
• Develop ML models and data pipelines for training and production with DevOps/MLOps.
• Customize Deep Learning/Gen AI models for client needs.
• Perform R&D on new AI/HP compute algorithms and simulations for client use.
• Work with large-scale datasets and data preprocessing for high-quality input.
• Implement efficient data storage/retrieval for models and knowledge.
• Justify model approaches in business terms.
• Collaborate with business and technical teams to deliver end-to-end project goals.

Skills

Production ML deployment

CS fundamentals

Distributed computing

Cloud deployment

Python

LLMs

Programming languages

Communication

Client-facing

Cross-functional

Entrepreneurship

Advanced degree

Education

Bachelor's Degree or equivalent

Tools

TensorFlow

PyTorch

Job description

Accenture is seeking an AI Engineer to design, build, and operationalize AI/ML solutions for enterprise clients in London. You will combine custom models with cloud services to deliver production-ready outcomes, addressing client needs from data assessment to deployment, with a focus on scalable data pipelines and DevOps for ML.

The role spans model selection, customization of Deep Learning and Gen AI, and collaboration with cross-functional teams to ensure value and reliability of deployed