4 of 4 Remote/Hybrid Contract Natural Language Processing Jobs

science fundamentals and time-series forecasting. Machine Learning: Strong grasp of ML and deep learning algorithms (e.g. Logistic Regression, Random Forest, XGBoost, BERT, LSTM, NLP, Transfer Learning). Reasonable Adjustments: Respect and equality are core values to us. We are proud of the diverse and inclusive community we have built ...

Skills & Experience Proven experience delivering AI, automation, or digital transformation programmes Strong understanding of AI/ML and intelligent automation technologies (e.g. RPA, NLP) Excellent stakeholder management and communication skills Experience working in Agile delivery environments Ability to bridge the gap between technical teams and business leaders Clearance Active ...

well-defined modelling tasks. Build end to end ML pipelinesfrom feature engineering and preprocessing to deployment ready model outputs. Apply advanced techniques such as NLP, time series forecasting, anomaly detection, optimisation, or LLM/GenAI methods where relevant. Implement model evaluation frameworks using offline metrics, cross validation, online experiments … connectivity and Data Integrations Experience working with large, complex datasets, including data cleaning, feature engineering, and exploratory data analysis. Familiarity with LLMs, NLP techniques, and GenAI frameworks, including embeddings, prompt engineering, or fine tuning. Experience building end to end ML pipelines, including model validation, optimisation, deployment, and monitoring. Understanding ...

development of automation processes. You will work with cutting-edge technology, integrating intelligent automation capabilities (such as Machine Learning, OCR, and NLP) into scalable workflows. Key Responsibilities Development: Design, develop, and implement enterprise-grade automations using Power Automate (cloud flows, desktop flows/RPA, and combined workflows). Solution Design … User Acceptance Testing (UAT), ensuring automations are robust, fit-for-purpose, and align with governance frameworks. Integration: Integrate intelligent automation capabilities (AI, OCR, NLP) into automated processes to enhance functionality. Handover: Manage the smooth transition of automations to the support team during Post Go-Live Support (PGLS), ensuring comprehensive knowledge ...