AI / ML Engineer

AI / ML Engineer

We are looking for a goal-oriented and driven AI/ML Engineer with strong experience in building, training, deploying, and scaling AI/ML applications. The ideal candidate will have a solid foundation in machine learning, neural networks,reinforcementlearning and deep learning, alongside hands-on experience with generative AI, agentic AI systems, RAG applications, LLM platforms, APIs, cloud deployment, and production-ready AI architectures.

Key Responsibilities

Machine Learning, Reinforcement Learning and Deep Learning Development

Design, develop, train, evaluate, and optimise machine learning, reinforcement learning and deep learning models for real-world business problems. Work with supervised and unsupervised learning techniques, feature engineering, model selection, hyperparameter tuning, and appropriate evaluation methodologies.

Develop and optimise neural network architectures using frameworks such as PyTorch and TensorFlow, with practical understanding of architectures including CNNs, RNNs/LSTMs, Transformers, embeddings, and other modern deep learning approaches where appropriate.

AI/ML Model Development

Develop, train, fine-tune, and optimise machine learning, Generative AI, and neural network models to meet specific business and functional requirements. Perform experimentation, model benchmarking, error analysis, and performance evaluation to ensure models are accurate, reliable, and suitable for production use.

Generative AI and Agentic AI Development

Design and build generative AI applications, agentic AI workflows, and multi-agent architectures using modern AI frameworks and orchestration tools.

RAG and GraphRAG Applications

Build Retrieval-Augmented Generation applications, including GraphRAG solutions using knowledge graphs, Neo4j, Astra DB, vector databases, embeddings, semantic search, reranking, and related retrieval technologies.

LLM Application Development

Work with both open-source and closed-source large language models to build scalable AI applications, including model routing, prompt engineering, fine-tuning, evaluation, benchmarking, and optimisation.

Voice-Based AI Implementation

Design and implement voice-based AI solutions, including speech-to-text, text-to-speech, conversational AI, and voice-enabled intelligent assistants.

API Development and Integration

Create robust API endpoints using tools such as FastAPI to enable seamless access to AI/ML models and integration with external systems and applications.

AI Platform Development

Architect and develop a user-friendly AI platform where multiple AI and machine learning models can be accessed, managed, deployed, and utilised through API calls.

System Design and Scalable Architecture

Contribute to the design of scalable, reliable AI systems, including queue-based processing, asynchronous workflows, distributed services, caching mechanisms, model-serving infrastructure, and production-grade backend architecture.

Model Training, Evaluation and Optimisation

Build repeatable ML experimentation and evaluation workflows. Apply appropriate metrics such as precision, recall, F1-score, ROC-AUC, regression metrics, ranking metrics, or task-specific evaluation methods.

Perform hyperparameter optimisation, model comparison, cross-validation, error analysis, regularisation, and techniques for improving model generalisation and inference performance.

LLM Performance and Caching Optimisation

Optimise LLM performance and scalability using caching mechanisms such as KV cache, response caching, prompt caching, batching, quantisation, and efficient model-serving strategies.

Observability and Monitoring

Implement observability, logging, tracing, monitoring, and evaluation workflows using tools such as Langfuse and related platforms to track model and system performance, reliability, cost, latency, and user interactions.

Monitor deployed ML models for performance degradation, data/model drift, and other production issues where appropriate.

Cloud Deployment and Infrastructure

Deploy AI/ML applications and models across different cloud providers and server environments, ensuring scalability, reliability, security, and performance.

Continuous Improvement

Continuously monitor, retrain, update, and improve models, APIs, workflows, and platforms based on user feedback, model performance, new data, and evolving AI technologies.

Skills and Qualifications

• Minimum 5years of experience building AI/ML software and production-ready AI applications.
• Strong understanding of fundamental machine learning concepts, including supervised learning, unsupervised learning, classification, regression, clustering, dimensionality reduction, feature engineering, model selection, and model evaluation.
• Strong expertise in neural networks and deep learning, with practical experience developing and training deep learning models.
• Understanding of modern deep learning architectures such as CNNs, RNNs/LSTMs, Transformers, attention mechanisms, embeddings, and transfer learning.
• Hands-on experience with model training, hyperparameter tuning, optimisation, regularisation, cross-validation, experimentation, and performance evaluation.
• Strong understanding of common ML evaluation metrics and the ability to select appropriate evaluation strategies based on the problem being solved.
• Proficiency in Python and AI/ML libraries and frameworks such as PyTorch, TensorFlow, scikit-learn, NumPy, Pandas, and related tools.
• Strong experience with Generative AI frameworks and technologies such as LangChain, LangGraph, FastAPI, and related tools.
• Experience with agentic AI, multi-agent architecture, RAG, GraphRAG, and LLM-based application development.
• Hands-on experience with Langfuse, LiteLLM, observability tools, tracing, model monitoring, and AI evaluation workflows.
• Experience working with queues, asynchronous processing, caching mechanisms, scalable system design, and backend architecture.
• Strong understanding of knowledge graphs, vector databases, Neo4j, Astra DB, embeddings, semantic search, and graph-based retrieval systems.
• Experience with both open-source and closed-source LLMs.
• Experience deploying and serving AI/ML models across different cloud providers and server environments.
• Understanding of model inference optimisation techniques such as batching, quantisation, GPU utilisation, and efficient model serving is desirable.
• Good understanding of software engineering best practices, including clean code, testing, documentation, CI/CD, version control, reproducible ML experiments, and maintainable system design.
• Excellent problem-solving abilities with strong analytical skills and attention to detail.
• Strong communication skills and the ability to collaborate effectively in a team-oriented environment.

Bonus / Preferred Experience

• Experience implementing voice-based AI applications, including conversational AI, speech-to-text, text-to-speech, and voice assistant technologies.
• Experience training or fine-tuning deep learning models using GPUs and distributed computing environments.
• Experience with transfer learning, fine-tuning, model distillation, quantisation, or other model optimisation techniques.
• Experience with Reinforcement Learning, Transformers and Neural Networks
• Familiarity with ML experiment tracking and model lifecycle tools such as MLflow, Weights & Biases, or equivalent platforms.
• Understanding of MLOps practices, including model versioning, model registries, automated training/evaluation pipelines, deployment, monitoring, and retraining.
• Experience scaling LLM applications using caching mechanisms such as KV cache, prompt caching, response caching, batching, and efficient inference strategies.
• Experience working across multiple cloud providers.
• Experience integrating both open-source and closed-source LLMs into production applications.
• Experience with advanced LLM operations, including model routing, cost optimisation, monitoring, evaluation, and performance tuning.

Educational Requirements

Doctoral or Master’s degree in Computer Science, Engineering, Artificial Intelligence, Machine Learning, Data Science, or a related field, preferably with coursework or practical experience in machine learning, deep learning, statistics, or applied AI.

Working Hours

Candidates must be available to work core UK business hours, 09:00-17:30 GMT/BST, Monday-Friday on site.