{"id":1459707,"url":"https://alion.io/job/awtg-ai-ml-engineer","title":"AI / ML Engineer","company":{"id":1907331,"name":"AWTG","domain":"awtg.co.uk","url":"https://alion.io/company/awtg","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Schema","truth_index":{"grade":"D","score":43,"open_postings":43,"ghost_share":0.953,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-01T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Embeddings","optional":false},{"name":"FastAPI","optional":false},{"name":"Fine-tuning","optional":false},{"name":"GraphRAG","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"KV Cache","optional":false},{"name":"LangChain","optional":false},{"name":"Langfuse","optional":false},{"name":"LangGraph","optional":false},{"name":"LiteLLM","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"Neo4j","optional":false},{"name":"NumPy","optional":false},{"name":"Pandas","optional":false},{"name":"Prompt Caching","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Reranking","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Semantic Search","optional":false},{"name":"Semantic Search","optional":false},{"name":"TensorFlow","optional":false},{"name":"Text-to-Speech","optional":false},{"name":"Transfer Learning","optional":false},{"name":"Transformers","optional":false},{"name":"CI/CD","optional":true},{"name":"MLFlow","optional":true},{"name":"Model Distillation","optional":true},{"name":"Weights & Biases","optional":true}],"status":"live","first_seen_at":"2026-07-02T11:33:43Z","employer_posted_date":"2026-08-13","last_verified_at":"2026-09-29T18:21:10Z","board_verified":false,"closed_at":null,"days_open":91,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":90},"description":"AI / ML Engineer\nWe 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.\nKey Responsibilities\nMachine Learning, Reinforcement Learning and Deep Learning Development\nDesign, 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.\nDevelop 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.\nAI/ML Model Development\nDevelop, 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.\nGenerative AI and Agentic AI Development\nDesign and build generative AI applications, agentic AI workflows, and multi-agent architectures using modern AI frameworks and orchestration tools.\nRAG and GraphRAG Applications\nBuild Retrieval-Augmented Generation applications, including GraphRAG solutions using knowledge graphs, Neo4j, Astra DB, vector databases, embeddings, semantic search, reranking, and related retrieval technologies.\nLLM Application Development\nWork 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.\nVoice-Based AI Implementation\nDesign and implement voice-based AI solutions, including speech-to-text, text-to-speech, conversational AI, and voice-enabled intelligent assistants.\nAPI Development and Integration\nCreate robust API endpoints using tools such as FastAPI to enable seamless access to AI/ML models and integration with external systems and applications.\nAI Platform Development\nArchitect and develop a user-friendly AI platform where multiple AI and machine learning models can be accessed, managed, deployed, and utilised through API calls.\nSystem Design and Scalable Architecture\nContribute 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.\nModel Training, Evaluation and Optimisation\nBuild 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.\nPerform hyperparameter optimisation, model comparison, cross-validation, error analysis, regularisation, and techniques for improving model generalisation and inference performance.\nLLM Performance and Caching Optimisation\nOptimise LLM performance and scalability using caching mechanisms such as KV cache, response caching, prompt caching, batching, quantisation, and efficient model-serving strategies.\nObservability and Monitoring\nImplement 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.\nMonitor deployed ML models for performance degradation, data/model drift, and other production issues where appropriate.\nCloud Deployment and Infrastructure\nDeploy AI/ML applications and models across different cloud providers and server environments, ensuring scalability, reliability, security, and performance.\nContinuous Improvement\nContinuously monitor, retrain, update, and improve models, APIs, workflows, and platforms based on user feedback, model performance, new data, and evolving AI technologies.\nSkills and Qualifications\nMinimum 5years of experience building AI/ML software and production-ready AI applications.\nStrong understanding of fundamental machine learning concepts, including supervised learning, unsupervised learning, classification, regression, clustering, dimensionality reduction, feature engineering, model selection, and model evaluation.\nStrong expertise in neural networks and deep learning, with practical experience developing and training deep learning models.\nUnderstanding of modern deep learning architectures such as CNNs, RNNs/LSTMs, Transformers, attention mechanisms, embeddings, and transfer learning.\nHands-on experience with model training, hyperparameter tuning, optimisation, regularisation, cross-validation, experimentation, and performance evaluation.\nStrong understanding of common ML evaluation metrics and the ability to select appropriate evaluation strategies based on the problem being solved.\nProficiency in Python and AI/ML libraries and frameworks such as PyTorch, TensorFlow, scikit-learn, NumPy, Pandas, and related tools.\nStrong experience with Generative AI frameworks and technologies such as LangChain, LangGraph, FastAPI, and related tools.\nExperience with agentic AI, multi-agent architecture, RAG, GraphRAG, and LLM-based application development.\nHands-on experience with Langfuse, LiteLLM, observability tools, tracing, model monitoring, and AI evaluation workflows.\nExperience working with queues, asynchronous processing, caching mechanisms, scalable system design, and backend architecture.\nStrong understanding of knowledge graphs, vector databases, Neo4j, Astra DB, embeddings, semantic search, and graph-based retrieval systems.\nExperience with both open-source and closed-source LLMs.\nExperience deploying and serving AI/ML models across different cloud providers and server environments.\nUnderstanding of model inference optimisation techniques such as batching, quantisation, GPU utilisation, and efficient model serving is desirable.\nGood understanding of software engineering best practices, including clean code, testing, documentation, CI/CD, version control, reproducible ML experiments, and maintainable system design.\nExcellent problem-solving abilities with strong analytical skills and attention to detail.\nStrong communication skills and the ability to collaborate effectively in a team-oriented environment.\nBonus / Preferred Experience\nExperience implementing voice-based AI applications, including conversational AI, speech-to-text, text-to-speech, and voice assistant technologies.\nExperience training or fine-tuning deep learning models using GPUs and distributed computing environments.\nExperience with transfer learning, fine-tuning, model distillation, quantisation, or other model optimisation techniques.\nExperience with Reinforcement Learning, Transformers and Neural Networks\nFamiliarity with ML experiment tracking and model lifecycle tools such as MLflow, Weights & Biases, or equivalent platforms.\nUnderstanding of MLOps practices, including model versioning, model registries, automated training/evaluation pipelines, deployment, monitoring, and retraining.\nExperience scaling LLM applications using caching mechanisms such as KV cache, prompt caching, response caching, batching, and efficient inference strategies.\nExperience working across multiple cloud providers.\nExperience integrating both open-source and closed-source LLMs into production applications.\nExperience with advanced LLM operations, including model routing, cost optimisation, monitoring, evaluation, and performance tuning.\nEducational Requirements\nDoctoral 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.\nWorking Hours\nCandidates must be available to work core UK business hours, 09:00-17:30 GMT/BST, Monday-Friday on site.","description_format":"text","description_chars":8357,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"master","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Cybersecurity","Information Technology","Science & Engineering"],"lifecycle":[{"event":"open","at":"2026-09-29T11:41:06Z"}],"liveness":{"score":9,"band":"cold","label":"Long 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