AI / ML Architect
Location: London (Hybrid - 2 days/week onsite)
Duration: 6 months
Day Rate: £510 per day (Inside IR35)
Role Overview
We are seeking an experienced AI / ML Architect to design, build, and lead the deployment of advanced machine learning and AI-driven solutions. The successful consultant will enable real-world value from data by architecting models, pipelines, and integration patterns that scale across the organisation.
This role combines technical depth, applied ML engineering, solution design, and hands-on development.
Key Responsibilities Design, build, and train end-to-end machine learning models across NLP, predictive analytics, classification, and computer vision use cases Experiment with algorithms, optimise hyperparameters, and evaluate model performance Collect, process, and prepare structured and unstructured datasets for model training and validation Implement feature engineering, data augmentation, and data quality controls Develop APIs, services, or microservices to integrate models into production platforms Deploy, monitor, and manage models using MLOps tooling, automation, and versioning practices Analyse performance metrics (accuracy, precision, recall, F1-score) and optimise models for scalability and efficiency Collaborate with product teams, engineers, and data scientists to translate business requirements into technical solutions Document architectures, workflows, and methodologies to ensure reproducibility and maintainability Stay current on emerging AI technologies including generative models, reinforcement learning, and transformer-based architectures
Profile & Required Expertise 10+ years software engineering experience, including 5+ years in applied AI/ML Advanced proficiency in Python, PyTorch, TensorFlow, and modern NLP frameworks Hands-on experience with LLMs, transformer architectures, LangChain, and Hugging Face Strong knowledge of algorithms, machine learning lifecycles, and model evaluation techniques Deep understanding of BFSI environments including risk, compliance, and AML/KYC Practical experience with cloud platforms (AWS/Azure/GCP) and containerisation (Docker/Kubernetes) Experience building scalable ML systems and secure, production-grade applications Proven ability to work collaboratively in cross-functional teams Strong communication skills with the ability to present technical concepts to non-technical stakeholders Demonstrated ability to write clear documentation and apply engineering best practices
Desirable Experience Ability to align ML strategies with long-term business and platform objectives Familiarity with emerging trends in generative AI, RAG workflows, or reinforcement learning Experience influencing architecture decisions at enterprise scale
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