Production AI Engineer – Multi-Agent & RAG Pipelines

Archer; Newcastle upon Tyne; On-site; GBP 110,000 - 160,000; Full time

Job summary

Archer is partnering with Accenture to identify and deliver enterprise-grade agentic AI solutions. The role focuses on designing, building, and deploying production-grade agentic architectures across the full tech stack, collaborating directly with client engineering teams and driving scalable patterns and accelerators.

Ideal candidates have hands-on experience shipping multi-agent systems in production, strong backend skills, and familiarity with LLM providers and orchestration frameworks.

Responsibilities

• Design and build production-grade agentic solutions end-to-end: multi-agent orchestration, RAG pipelines, policy-based routing, tool invocation, memory management, and lifecycle observability
• Build and own RAG pipelines: embeddings, chunking strategy, vector search, context window engineering and tuning against real quality targets
• Integrate and abstract across multiple LLM providers — OpenAI, Anthropic, Vertex AI, and open-source models — with fallback routing, token, cost, and latency management
• Implement LLMOps in production: eval harnesses with real quality metrics, prompt versioning, observability tooling, cost and safety monitoring
• Embed directly with client engineering teams to analyse the SDLC to identify AI/agentic opportunities and use cases, design, prototype, and deploy agentic solutions
• Build reusable patterns, accelerators, and playbooks that scale beyond the individual client engagement and enable the next one to start faster
• Define and use metrics to measure agent accuracy, latency, safety, and cost-effectiveness; present findings and recommendations to client stakeholders in business terms
• Significant software engineering experience in production environments
• Hands‑on experience designing and deploying agentic AI solutions in a production environment
• Demonstrated experience with agentic orchestration frameworks: LangGraph, CrewAI, AutoGen, or equivalent — at production depth, not tutorial level
• Direct experience calling LLM APIs in production code: provider abstraction, token management, latency and cost tradeoffs
• RAG pipeline ownership: embeddings, chunking strategy, vector databases, and context engineering
• LLMOps fundamentals: eval harness design, prompt versioning, and production observability
• Cloud‑native engineering maturity: Kubernetes, Docker, microservices, serverless, CI/CD, and IaC
• Strong Python; Java or equivalent backend language acceptable; production debugging and observability experience
• Quality of experience is weighted over years, a candidate who has shipped three production agentic systems in four years is preferred over a generalist with passive AI exposure

Job description

Archer is partnering with Accenture to identify and deliver enterprise-grade agentic AI solutions. The role focuses on designing, building, and deploying production-grade agentic architectures across the full tech stack, collaborating directly with client engineering teams and driving scalable patterns and accelerators.

Ideal candidates have hands-on experience shipping multi-agent systems in production, strong backend skills, and familiarity with LLM providers and orchestration frameworks.