Senior AI Engineer: Vision, GenAI & MLOps

Cognizant; Greater London; On-site; GBP 60,000 - 100,000; Full time

Job summary

Cognizant is seeking an AI/ML engineer to work with Studios, ML Engineers and Product teams in shaping next‑gen AI-powered automation for fashion and retail applications. You will apply computer vision to product imagery and external inspiration to create smarter image generation and personalized experiences.

The role involves building end‑to‑end ML/AI solutions, developing data pipelines, standardising workflows, and embedding responsible AI practices across cross‑functional teams in a

Qualifications

• Must have hands-on experience in building and deploying ML/CV solutions end-to-end.
• Experience with fine-tuning models for specific use cases.
• Familiarity with CI/CD, containerization, monitoring in MLOps.
• Strong collaboration across multidisciplinary teams.

Responsibilities

• Design and develop AI solutions using LLMs and generative AI.
• Build scalable data pipelines and ensure data quality.
• Standardise and reuse AI workflows across domains.
• Apply MLOps practices to AI systems including CI/CD and monitoring.
• Collaborate with Cloud Infrastructure to enable secure, cost-effective AI environments.
• Embed ethical and responsible AI practices to mitigate bias and risk.
• Fine-tune pre-trained models for relevance and performance.
• Contribute to evaluation frameworks with automated tests and benchmarking.

Skills

Python

ML/DL libraries

Computer vision

Generative AI

Prompting & RAG

MLOps & CI/CD

Model fine-tuning

Team collaboration

Tools

PyTorch

TensorFlow

Hugging Face

Docker

Kubernetes

Job description

Cognizant is seeking an AI/ML engineer to work with Studios, ML Engineers and Product teams in shaping next‑gen AI-powered automation for fashion and retail applications. You will apply computer vision to product imagery and external inspiration to create smarter image generation and personalized experiences.

The role involves building end‑to‑end ML/AI solutions, developing data pipelines, standardising workflows, and embedding responsible AI practices across cross‑functional teams in a