AI Engineer (Contract) - AI Cloud
Hamilton Barnes
Charing Cross, United Kingdom
3 days ago
Role details
Contract type
Contract Employment type
Full-time (> 32 hours) Working hours
Regular working hours Languages
English Experience level
Senior Compensation
£ 124KJob location
Charing Cross, United Kingdom
Tech stack
API
Artificial Intelligence
Application Integration Architecture
Google BigQuery
Cloud Computing
Continuous Integration
Data Flow Control
Python
Machine Learning
Open Source Technology
Performance Tuning
TensorFlow
Prometheus
PyTorch
Large Language Models
Grafana
Generative AI
Build Management
Containerization
Kubernetes
HuggingFace
Machine Learning Operations
Api Design
Data Pipelines
Dynatrace
Docker
Microservices
Job description
Location: London, Manchester, Bristol, Leeds, Edinburgh (Hybrid - 2 days onsite)
Duration: 6 months Rate: £475/day
The Role
We're looking for an AI Engineer to design, build, and deploy scalable AI/ML and GenAI solutions. You'll work across the full life cycle-from data pipelines to production-delivering real-world impact in modern cloud environments.
Key Responsibilities
- Build and deploy end-to-end AI/ML solutions
- Develop LLM/GenAI applications (prompting, fine-tuning, RAG pipelines)
- Optimise model performance (latency, scalability, cost)
- Design APIs and microservices for AI integration
- Implement MLOps/LLMOps pipelines (CI/CD, deployment, monitoring)
- Ensure Responsible AI and production reliability
Required Skills
- 5-12 years in AI/ML engineering
- Strong Python experience
- Hands-on with LLMs/GenAI (eg Gemini, open-source models)
- Experience with RAG, embeddings, and vector databases
- API development & microservices architecture
- CI/CD and containerisation (Docker, Kubernetes)
- GCP experience (BigQuery, Vertex AI, Dataflow, Pub/Sub)
Nice to Have
- TensorFlow/PyTorch/Hugging Face
- MLOps/LLMOps best practices
- Observability tools (Prometheus, Dynatrace, LangSmith)
- Security, governance, and performance optimisation experience
Requirements
- 5-12 years in AI/ML engineering
- Strong Python experience
- Hands-on with LLMs/GenAI (eg Gemini, open-source models)
- Experience with RAG, embeddings, and vector databases
- API development & microservices architecture
- CI/CD and containerisation (Docker, Kubernetes)
- GCP experience (BigQuery, Vertex AI, Dataflow, Pub/Sub)
Nice to Have
- TensorFlow/PyTorch/Hugging Face
- MLOps/LLMOps best practices
- Observability tools (Prometheus, Dynatrace, LangSmith)
- Security, governance, and performance optimisation experience
