{ “@context”: “http://schema.org”, “@type”: “JobPosting”, “title”: “Gen-AI Prompt & Context Engineer(Contract) UK Remote | Outside IR35 | £400/day |”, “description”: “
We are seeking a Gen-AI Prompt & Context Engineer to design and operate end-to-end LLM workflows, including prompt strategy, retrieval pipelines, multi-agent orchestration, and production observability. The role involves building scalable AI automation using Python, CrewAI multi-agent systems, and n8n workflows while ensuring safety, governance, and performance.
Key Responsibilities
- Design and optimize prompts, templates, and prompt libraries for consistent AI outputs
- Build context pipelines (RAG, embeddings, chunking, hybrid search) and maintain knowledge freshness
- Develop and orchestrate multi-agent workflows using CrewAI and integrate external tools/APIs
- Create automated LLM workflows in n8n with retries, approvals, and monitoring
- Build Python services, evaluation harnesses, and metrics pipelines for LLM performance
- Implement guardrails, RBAC, audit trails, and compliance-aligned AI governance
- Monitor production reliability with tracing, dashboards, alerts, and fallback strategies
Required Skills
- Strong Python engineering and API development experience
- Hands-on experience with prompt engineering, RAG pipelines, and LLM orchestration
- Experience with multi-agent frameworks (e.g., CrewAI) and workflow tools like n8n
- Familiarity with vector databases, embeddings, and hybrid retrieval approaches
- Knowledge of observability, evaluation methods, and AI safety controls
- Strong problem-solving skills and ability to work cross-functionally
Nice to Have
- Experience with OpenTelemetry, Grafana, or similar monitoring tools
- Exposure to enterprise AI governance, privacy, or regulated environments
- Experience optimizing LLM cost, latency, and accuracy in production systems
Company: 9NEXUS
Location:
Job Description:
We are seeking a Gen-AI Prompt & Context Engineer to design and operate end-to-end LLM workflows, including prompt strategy, retrieval pipelines, multi-agent orchestration, and production observability. The role involves building scalable AI automation using Python, CrewAI multi-agent systems, and n8n workflows while ensuring safety, governance, and performance.
Key Responsibilities
- Design and optimize prompts, templates, and prompt libraries for consistent AI outputs
- Build context pipelines (RAG, embeddings, chunking, hybrid search) and maintain knowledge freshness
- Develop and orchestrate multi-agent workflows using CrewAI and integrate external tools/APIs
- Create automated LLM workflows in n8n with retries, approvals, and monitoring
- Build Python services, evaluation harnesses, and metrics pipelines for LLM performance
- Implement guardrails, RBAC, audit trails, and compliance-aligned AI governance
- Monitor production reliability with tracing, dashboards, alerts, and fallback strategies
Required Skills
- Strong Python engineering and API development experience
- Hands-on experience with prompt engineering, RAG pipelines, and LLM orchestration
- Experience with multi-agent frameworks (e.g., CrewAI) and workflow tools like n8n
- Familiarity with vector databases, embeddings, and hybrid retrieval approaches
- Knowledge of observability, evaluation methods, and AI safety controls
- Strong problem-solving skills and ability to work cross-functionally
Nice to Have
- Experience with OpenTelemetry, Grafana, or similar monitoring tools
- Exposure to enterprise AI governance, privacy, or regulated environments
- Experience optimizing LLM cost, latency, and accuracy in production systems
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Posted: April 17th, 2026