At a Glance
- Tasks: Develop and optimise cutting-edge AI/ML applications using the latest technologies.
- Company: Join a forward-thinking tech company focused on innovation and collaboration.
- Benefits: Enjoy competitive pay, health perks, remote work options, and growth opportunities.
- Other info: Dynamic team environment with endless learning and career advancement potential.
- Why this job: Be at the forefront of AI technology and make a real difference in the industry.
- Qualifications: 3+ years in AI/ML development with strong Python and framework skills.
The predicted salary is between 70000 - 90000 £ per year.
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.
Key Responsibilities
- 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.
- 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, 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, evaluation, 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 models and integration with external systems and applications.
- AI Platform Development: Architect and develop a user-friendly AI platform where multiple AI models can be accessed, managed, 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, and production-grade backend architecture.
- LLM Performance and Caching Optimisation: Optimise LLM performance and scalability using caching mechanisms such as KV cache, response caching, prompt caching, 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 system performance, reliability, cost, and user interactions.
- Cloud Deployment and Infrastructure: Deploy AI/ML applications across different cloud providers and server environments, ensuring scalability, reliability, security, and performance.
- Continuous Improvement: Continuously monitor, update, and improve models, APIs, workflows, and platforms based on user feedback, system performance, and evolving AI technologies.
Skills And Qualifications
- 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.
Bonus / Preferred Experience
- 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.
Educational Requirements
- Bachelor’s or Master’s degree in Computer Science, Engineering, Artificial Intelligence, Data Science, or a related field, with a focus on AI/ML.
Working Hours
- Candidates must be available to work core UK business hours, 9:00–17:30 GMT/BST, Monday–Friday.
AI / ML Engineer employer: AWTG Ltd
As a leading employer in the data science field, we offer a dynamic remote work environment that fosters collaboration across multidisciplinary teams. Our commitment to employee growth is evident through mentorship opportunities and a focus on responsible AI practices, ensuring that our team members not only excel in their roles but also contribute to meaningful outcomes for clients. Join us to be part of a culture that values innovation, ethical practices, and the continuous development of your skills.
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We think this is how you could land AI / ML Engineer
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Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like AWTG Ltd.
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We think you need these skills to ace AI / ML Engineer
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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Craft a Tailored Cover Letter:For a full-time role at AWTG Ltd, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at AWTG Ltd. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at AWTG Ltd
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
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Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at AWTG Ltd!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
