AI Native SW Engineering Specialist / Sr Consultant

Accenture UK & Ireland; Birmingham; Sur place; GBP 90 000 - 140 000; Plein temps

Résumé du poste

Accenture UK & Ireland seeks an experienced AI Engineer to advance production-grade agentic systems for enterprise clients in Birmingham. You will own end-to-end design, implement RAG pipelines, integrate multiple LLM providers, and ensure observability and cost controls in production code.

The role requires hands-on experience shipping multi-agent systems, strong Python and backend skills, and familiarity with Kubernetes, Docker, and cloud-native patterns.

Qualifications

• Extensive production-grade software engineering experience.
• Hands-on experience shipping agentic AI solutions in production.
• Experience with agentic orchestration frameworks (LangGraph, CrewAI, AutoGen).
• Direct experience calling LLM APIs (OpenAI, Anthropic, Vertex AI) in production code.
• Strong Python and backend language skills; proficiency in debugging and observability.

Responsabilités

• Design and build production-grade agentic systems end-to-end: multi-agent orchestration and observability.
• Build and own RAG pipelines: embeddings, chunking, vector search, and context tuning.
• Integrate across LLM providers with fallback routing, token, cost, and latency management.
• Implement LLMOps in production: eval harnesses, prompt versioning, observability tooling.
• Collaborate with client teams for PoCs, code-with sessions, and architecture walkthroughs.
• Define metrics for agent accuracy, latency, safety, and cost; present to stakeholders.

Connaissances

Python

Backend development

Production debugging

LLMOps

System design

Cross-team collaboration

Outils

Kubernetes

Docker

LangGraph

CrewAI

AutoGen

LLM APIs

Description du poste

You build the systems that actually make AI work in enterprise environments, not demos, not prototypes that stall after a pilot, but production agentic architectures running inside real client organizations. The difference between an AI Engineer and what we are looking for is straightforward: you have shipped a multi-agent system in production, you have owned the eval harness, and you know what happens when your agent fails at 2am because you have lived it.

Key Responsibilities

• Design and build production-grade agentic systems 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 (LangSmith, Braintrust, or equivalent), cost and safety monitoring
• Embed directly with client engineering teams to design, prototype, and deploy agentic solutions - workshops, proofs of concept, code-with sessions, and architecture walkthroughs
• 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
• Extensive software engineering experience in production environments
• Hands-on experience designing and deploying agentic AI solutions in a production environment - non-negotiable
• Demonstrated experience with agentic orchestration frameworks: LangGraph, CrewAI, AutoGen, or equivalent - at production depth, not tutorial level
• Direct experience calling LLM APIs (OpenAI, Anthropic, Vertex AI) 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 (Terraform or Helm)
• 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