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Gen-AI Prompt & Context Engineer(Contract Remote | Outside IR35 | £400/day
Remote / Online - Candidates ideally in
Northampton, Northamptonshire, NN1, England, UK
Listed on 2026-08-14
Northampton, Northamptonshire, NN1, England, UK
Listing for:
9NEXUS
Contract, Remote/Work from Home
position Listed on 2026-08-14
Job specializations:
-
Software Development
AI Engineer (Applied/Software), AI Reliability/ Performance Engineer, Python, Machine Learning/ ML Engineer
Job Description & How to Apply Below
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
- 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
- Experience with Open Telemetry, 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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