AI Engineer (Contract) - AI Cloud
Posted 1 week ago by Hamilton Barnes
Negotiable
Inside
Hybrid
London, Manchester, Bristol, Leeds, Edinburgh , UK
Summary: The AI Engineer role involves designing, building, and deploying scalable AI/ML and GenAI solutions across the full life cycle, from data pipelines to production. The position requires collaboration in modern cloud environments to deliver impactful results. The role is hybrid, requiring two days on-site, and spans a duration of six months. The successful candidate will leverage their extensive experience in AI/ML engineering to optimize model performance and ensure responsible AI practices.
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
Key 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)
Salary (Rate): £475/day
City: London
Country: UK
Working Arrangements: hybrid
IR35 Status: inside IR35
Seniority Level: undetermined
Industry: IT
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