AI Software Engineering Manager

Accenture UK & Ireland; Newcastle upon Tyne; On-site; GBP 90,000 - 150,000; Full time

Job summary

Accenture UK & Ireland is seeking an AI Engineer (Agentic/Applied) to design, build, and deploy production-grade agentic AI solutions across enterprise stacks. You will collaborate with client engineering teams, lead technical design sessions, and create scalable patterns and accelerators for multiple engagements.

You will own LLMOps, define RAG standards, and drive production dashboards, governance, and cost controls across several concurrent systems.

Qualifications

• Extensive production software engineering experience in AI projects.
• Hands-on design and deployment of agentic AI solutions in production.
• Experience with agentic orchestration frameworks at production depth (LangGraph, CrewAI, AutoGen, or equivalent).
• Direct production experience calling LLM APIs (OpenAI, Anthropic, Vertex AI) with provider abstraction.
• RAG pipelines ownership including embeddings, chunking, vectors, and context engineering.
• LLMOps fundamentals: eval harness, prompt versioning, and production observability.
• Cloud-native maturity: Kubernetes, Docker, CI/CD, and IaC (Terraform/Helm).
• Strong Python; Java or equivalent backend language; production debugging and observability.
• Demonstrated ability to ship multiple production agentic systems and lead teams.
• People leadership: developing and managing engineers and conducting career conversations.

Responsibilities

• Architect and govern production-grade agentic solutions at enterprise scale: multi-agent orchestration, RAG pipelines, memory management, and lifecycle observability.
• Define RAG pipeline standards across engagements: chunking and embedding strategies, quality benchmarks, and metric-backed decisions.
• Set multi-LLM integration standards: vendor-agnostic architecture with fallback routing and cost governance across providers.
• Own LLMOps at programme scale: evaluation strategy, prompt governance, observability tooling standards, safety controls.
• Lead client engagements at senior level — analyse SDLC to identify AI opportunities, run architecture sessions, deliver PoCs.
• Publish reusable patterns, accelerators, and standards to scale the practice and reduce ramp-up time.
• Define programme-level AI impact in business terms to stakeholders; shape measurement frameworks for agentic systems.

Skills

Python

Cloud-native engineering

Kubernetes

Docker

LLMOps

LLM APIs

RAG pipelines

LangGraph

CrewAI

AutoGen

Observability

Production debugging

Leadership

Tools

Kubernetes

Docker

Terraform

Helm

OpenAI API

Anthropic API

Vertex AI

LangGraph

CrewAI

AutoGen

Job description

Please Note: Any offer of employment is subject to satisfactory BPSS and SC security clearance which typically requires 5 years continuous UK address history usually including no periods of 30 consecutive days or more spent outside of the UK.

Note: The above information relates to a specific client requirement

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.

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 (SDLC).

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

• Architect and govern production-grade agentic solutions at enterprise scale: multi-agent orchestration across complex environments, RAG pipelines, policy-based routing, memory management, and programme-level lifecycle observability
  
• Define RAG pipeline standards across engagements: establish chunking and embedding strategies, set quality benchmarks, and ensure metric-backed tradeoff decisions are documented and transferable
  
• Set multi-LLM integration standards: vendor-agnostic architecture by default, fallback routing and cost governance as standard design practice across providers including OpenAI, Anthropic, Vertex AI, and open-source models
  
• Own LLMOps at programme scale: evaluation strategy, prompt governance, observability tooling standards, safety monitoring and cost controls across multiple concurrent systems
  
• Lead client engineering engagements at senior level — analyse the SDLC to identify AI/agentic opportunities and use cases, facilitate architecture design sessions, lead proof-of-concept delivery, and drive alignment between client technology leadership and delivery teams
  
• Shape and publish reusable patterns, accelerators, and engineering standards that scale across the practice and reduce ramp-up time on new client engagements
  
• Own the measurement framework for agentic system quality: define accuracy, latency, safety, and cost metrics; present programme-level AI impact in business terms to senior client stakeholders
  
• 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
  
• People lead responsibilities: experience managing, developing, and performance-managing a team of engineers; setting individual development plans and conducting career conversations