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