Senior Developer - AI Enablement
Henderson Scott
London, UK
4 days ago
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Role details
Contract type
Temporary contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Working hours
Regular working hours
Job source
Tech stack
Application Programming Interfaces (APIs)
Artificial Intelligence
Amazon Web Services
Audit Trail
Automation of Tests
Continuous Integration
Information Leak Prevention
Identity and Access Management
Python (Programming Language)
Key Management
Live Connect (Windows)
Node.Js
+21 more
OAuth
OpenID
OpenAI
TypeScript
Spring Cloud
Retrieval-Augmented Generation
Large Language Models
Prompt Engineering
Backend
Agentic-AI
Rate Limiting
SC Clearance
Cloudflare
Claude
Evaluation of Large Language Models
Cloudwatch
Model Context Protocol
Terraform
Splunk
Docker
Microservices
Job description
- Design, build, test, and maintain Back End microservices and APIs powering the Enterprise AI Gateway.
- Integrate AWS Bedrock, AI Gateways, and approved foundation-model services into enterprise workflows.
- Build reusable APIs, libraries, and configuration patterns for consuming teams.
- Build authentication, authorisation (OIDC/OAuth2), rate limiting, usage attribution, and audit logging controls.
- Implement safe integrations with AI agents, tool calling, and Model Context Protocol (MCP) services.
Requirements
- Strong hands-on experience building production APIs/backend services in Python or TypeScript/Node.js.
- Practical experience integrating LLM APIs (eg, Anthropic Claude, OpenAI, Mistral) into applications or workflows.
- Solid understanding of prompt management, structured outputs, tool calling, context handling, and agentic patterns.
- Experience deploying cloud-native applications on AWS using automated CI/CD and Terraform/Docker.
- Experience with secrets management, IAM, automated testing, and building observable live services (OpenTelemetry, Splunk, CloudWatch).
- Active SC Clearance
Desirable Skills:
- Experience with Cloudflare AI Gateway or custom AI Proxy configurations.
- Hands-on experience with Model Context Protocol (MCP) or agentic frameworks.
- Knowledge of RAG, vector databases, embeddings, or LLM evaluation frameworks.
- Awareness of AI security threats (prompt injection, data leakage, unsafe tool execution).
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