AI Software Engineering Specialist

ACCENTURE; Newcastle Emlyn; On-site; GBP 90,000 - 130,000; Full time

Job summary

Accenture Newcastle is seeking an AI Engineer (Agentic/Applied) to design, build and deploy production-grade agentic AI solutions across the enterprise tech stack. You will work with client teams, lead technical design sessions, and develop reusable patterns that scale beyond individual engagements.

This role requires hands-on production experience with agentic orchestration, LLM APIs, and RAG pipelines, plus cloud-native skills (Kubernetes, Docker) and robust observability practices.

Qualifications

• Significant software engineering experience in production environments.
• Hands-on experience designing and deploying agentic AI solutions in production.
• Experience with agentic orchestration frameworks at production depth.
• Direct experience calling LLM APIs (OpenAI, Anthropic, Vertex AI) in production code.
• RAG pipeline ownership: embeddings, chunking, vector DBs, context engineering.
• LLMOps fundamentals: eval harness design, prompt versioning, observability.
• Cloud-native: Kubernetes, Docker, microservices, serverless, CI/CD, IaC.

Responsibilities

• Design and build production-grade agentic solutions end-to-end.
• Build and own RAG pipelines: embeddings, vector search, context tuning.
• Integrate and abstract across multiple LLM providers with proper routing.
• Implement LLMOps in production: eval harnesses, prompt versioning, observability.
• Collaborate with client teams to analyze SDLC and deploy agentic solutions.
• Create reusable patterns and playbooks to accelerate future engagements.
• Define metrics and report agent accuracy, latency, safety, and cost.

Skills

Production-grade software engineering

Agentic AI design & deployment

Agentic orchestration frameworks

LLM APIs in production

RAG pipelines and embeddings

LLMOps fundamentals

Cloud-native engineering

Python / backend languages

Production debugging & observability

Tools

LangGraph

CrewAI

AutoGen

OpenAI API

Vertex AI

Kubernetes

Docker

LangSmith / Braintrust observability

Job description

Job Description

You build the solutions 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 solution in production, you have owned the evaluation harness, and you know what happens when your agent fails at 2am because you have lived it.

As an AI Engineer (Agentic/Applied), you will design, build, and deploy production-grade agentic AI solutions across the full enterprise technology stack. You will work directly with client engineering teams, lead technical design sessions, and build reusable patterns and accelerators that scale beyond individual engagements to maximise efficiencies across the software delivery lifecycle.

This role sits at the heart of the AI engineering talent market - demand is growing faster than supply and will continue to do so. We offer what no single product company can: breadth across every industry, every enterprise technology stack, and every level of organizational complexity, combined with vendor fellowship access inside Anthropic, OpenAI, Microsoft, and Google engineering teams and a direct pathway to the Forward Deployed Engineer programme.

Key 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 (LangSmith, Braintrust, or equivalent), 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 - 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

Qualification

• Significant 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

Locations

Newcastle

Equal Employment Opportunity Statement

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About Accenture

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