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AI / ML Engineer
Job Title: AI/ML Engineer
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.
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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.
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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.
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Skills
AI/ML Applications
Generative AI
Agentic AI
RAG Applications
LLM Platforms
APIs
Cloud Deployment
Production-Ready Architectures
Python
TensorFlow
PyTorch
Neo4j
Astra DB
Observability
Caching Mechanisms
Model Monitoring
Location