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AWTG Logo AWTG · IT - Software Development

Ai / Ml Engineer

United Kingdom · Job Posted July 04, 2026
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Job Description

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

Job Responsibility

  • Develop, train, fine-tune, and optimise machine learning, Generative AI and neural network models to meet specific business and functional requirements
  • Design and build generative AI applications, agentic AI workflows, and multi-agent architectures using modern AI frameworks and orchestration tools
  • Build Retrieval-Augmented Generation applications, including GraphRAG solutions using knowledge graphs, Neo4j, Astra DB, vector databases, and related retrieval technologies
  • Work with both open-source and closed-source large language models to build scalable AI applications, including model routing, prompt engineering, evaluation, and optimisation
  • Design and implement voice-based AI solutions, including speech-to-text, text-to-speech, conversational AI, and voice-enabled intelligent assistants
  • Create robust API endpoints using tools such as FastAPI to enable seamless access to AI models and integration with external systems and applications
  • Architect and develop a user-friendly AI platform where multiple AI models can be accessed, managed, and utilised through API calls
  • Contribute to the design of scalable, reliable AI systems, including queue-based processing, asynchronous workflows, distributed services, caching mechanisms, and production-grade backend architecture
  • Optimise LLM performance and scalability using caching mechanisms such as KV cache, response caching, prompt caching, and efficient model-serving strategies
  • Implement observability, logging, tracing, monitoring, and evaluation workflows using tools such as Langfuse and related platforms to track system performance, reliability, cost, and user interactions
  • Deploy AI/ML applications across different cloud providers and server environments, ensuring scalability, reliability, security, and performance
  • Continuously monitor, update, and improve models, APIs, workflows, and platforms based on user feedback, system performance, and evolving AI technologies

Requirements

  • Minimum 3 years of experience in building AI/ML software and production-ready AI applications
  • Strong expertise in machine learning, neural networks, deep learning, and generative AI applications
  • Proficiency in Python and AI/ML frameworks such as TensorFlow, PyTorch, NumPy, 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, and graph-based retrieval systems
  • Experience with both open-source and closed-source LLMs
  • Experience deploying AI applications across different cloud providers and server environments
  • Good understanding of software engineering best practices, including clean code, testing, documentation, CI/CD, version control, and maintainable system design
  • Excellent problem-solving abilities with strong attention to detail
  • Strong communication skills and the ability to collaborate effectively in a team-oriented environment

Nice to have

  • Experience implementing voice-based AI applications, including conversational AI, speech-to-text, text-to-speech, and voice assistant technologies
  • Experience scaling LLM applications using caching mechanisms such as KV cache, prompt caching, response caching, 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, and performance tuning
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