AI Software Engineering Manager
Newcastle, Northern Ireland, United Kingdom · Full Time · Posted
About the role
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. 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 (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
#LI-EU
About Accenture
Accenture is a leading global professional services company that helps the
world’s leading businesses, governments and other organizations build their
digital core, optimize their operations, accelerate revenue growth and enhance
citizen services—creating tangible value at speed and scale. We are a talent-
and innovation-led company with approximately 791,000 people serving clients in
more than 120 countries. Technology is at the core of change today, and we are
one of the world’s leaders in helping drive that change, with strong ecosystem
relationships. We combine our strength in technology and leadership in cloud,
data and AI with unmatched industry experience, functional expertise and global
delivery capability. Our broad range of services, solutions and assets across
Strategy & Consulting, Technology, Operations, Industry X and Song, together
with our culture of shared success and commitment to creating 360° value, enable
us to help our clients reinvent and build trusted, lasting relationships. We
measure our success by the 360° value we create for our clients, each other, our
shareholders, partners and communities.
Visit us at www.accenture.com [http://www.accenture.com/]
Equal Employment Opportunity Statement
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