> Markdown version of [/jobs/ext/3901782-ml-engineer](https://www.wearedevelopers.com/jobs/ext/3901782-ml-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # ML Engineer - **Company:** cognizant - **Location:** Boveney, UK - **Experience:** Experienced - **Salary:** £52,559.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Computer Vision, Microsoft Azure, Data Cleansing, Python (Programming Language), Machine Learning, Natural Language Processing, Tensorflow, Reinforcement Learning, Supervised Learning, Google Cloud, Data Ingestion, Pytorch, Deep Learning, Generative AI, Kubernetes, Information Technology, DataRobot, Variational Autoencoders, Machine Learning Operations, Feature Extraction, GPT, Docker, Unsupervised Learning - **Published:** October 24, 2026 - **Apply:** https://www.adzuna.co.uk/jobs/details/5210481464 ## About the Role * Master's degree in Computer Science, Machine Learning, Data Science, or related fields * 3+ years of experience in developing machine learning systems using Python, TensorFlow, PyTorch, or similar frameworks. * Deep knowledge of math, probability, statistics, and algorithms * Solid understanding of machine learning concepts such as supervised learning, unsupervised learning, deep learning, natural language processing, computer vision, etc. * Hands-on experience in implementing generative models such as GANs, VAEs, Transformers, etc. * Experience in generating content using various modalities such as text, images, music, code, etc. * Experience in deploying machine learning models on cloud platforms such as AWS, Azure, GCP, etc. * Proficiency in machine learning frameworks (e.g., TensorFlow, PyTorch). * Experience optimizing model hyperparameter tuning for speed and cost * Experience in building MLOps pipelines using MLOps frameworks like Kubeflow, MLFlow, and DataRobot is desired. * Experience with Docker and Kubernetes * Familiarity with Eventbridge and AWS step function for workload orchestration * Excellent analytical and problem-solving skills, with the ability to think critically and creatively to develop innovative AI solutions. ## Description * Design, develop, evaluate, benchmark, and implement generative AI models and algorithms, utilizing state-of-the-art techniques such as GPT, VAE, GAN, and reinforcement learning. * Collaborate with cross-functional teams to define project goals, research requirements, and develop innovative solutions, ensuring alignment with overall business goals. * Optimize model through experimentation, hyperparameter tuning, and advanced optimization techniques for improved performance, scalability, and efficiency. * Stay up-to-date on the latest advancements in generative AI, deep learning, and related fields, and incorporate new techniques and methods into the team's workflow. * Develop and maintain AI pipelines for data preprocessing, feature extraction, model training, evaluation, and deployment. * Generate high-quality content using various modalities such as text, images, music, code, etc. * Conduct experiments and analysis to evaluate the performance and quality of the generated content. * Manage data ingestion processes, oversee labelling efforts, and monitor data quality to support your own machine-learning model. * Develop and maintain clear and concise documentation (including technical specifications, user guides, and presentations) of generative AI models, processes, and results to communicate complex AI concepts to both technical and non-technical stakeholders. * Contribute to the establishment of best practices and standards for generative AI development within the organization. * Provide technical mentorship and guidance to junior team members.