Accenture UK & Ireland
AI Native Engineer (Agentic / Applied)
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AI Engineer (Agentic/Applied)
You build the systems that actually make AI work in enterprise environments—not just demos or prototypes, but production agentic architectures running inside real client organizations.
The difference between an AI Engineer and the ideal candidate here is clear: ✔ You’ve shipped multi-agent systems in production ✔ You’ve owned the eval harness ✔ You know what happens when your system fails at 2am—because you’ve lived it
As an AI Engineer (Agentic/Applied), you’ll:
- Design, build, and deploy production-grade agentic AI systems across the full enterprise technology stack
- Work directly with client engineering teams
- Lead technical design sessions
- Develop reusable patterns and accelerators that scale beyond single engagements
This role sits at the heart of the AI engineering talent market—with rising demand faster than supply. We offer what no single product company can: 🔹 Breadth across every industry, enterprise tech stack, and organizational complexity 🔹 Vendor access inside Anthropic, OpenAI, Microsoft, and Google engineering teams 🔹 A pathway to the Forward Deployed Engineer programme
Key Responsibilities
- Architect and govern production-grade agentic systems at enterprise scale:
- Multi-agent orchestration across complex environments
- RAG (Retrieval-Augmented Generation) pipelines
- Policy-based routing
- Memory management
- Programme-level lifecycle observability
- Define RAG pipeline standards across engagements:
- Establish chunking and embedding strategies
- Set quality benchmarks
- Document and transfer metric-backed tradeoff decisions
- Set multi-LLM integration standards with a vendor-agnostic approach:
- Fallback routing
- Cost governance as standard practice
- Compatibility with OpenAI, Anthropic, Vertex AI, open-source models
- Own LLMOps (Large Language Model Operations) at programme scale:
- Eval strategy
- Prompt governance
- Observability tooling standards
- Safety monitoring & cost controls
- Lead client engineering engagements at a senior level:
- Facilitate architecture design sessions
- Deliver proofs-of-concept
- Align client technology leadership with delivery teams
- Shape and publish reusable patterns, accelerators, and engineering standards that:
- Scale across the practice
- Reduce ramp-up time on new engagements
- Own the measurement framework for agentic system quality:
- Define accuracy, latency, safety, and cost metrics
- Present AI impact in business terms to senior stakeholders
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Basic Qualifications
- Software engineering experience in production environments
- Hands-on experience designing/deploying agentic AI solutions (non-negotiable)
- Production depth experience with agentic orchestration frameworks:
- LangGraph
- CrewAI
- AutoGen (or equivalent—not tutorial-level)
- Direct API experience calling OpenAI, Anthropic, Vertex AI in production:
- Vendor abstraction
- Token management
- Latency & cost tradeoffs
- Full ownership of RAG pipelines:
- Embeddings
- Chunking strategy
- Vector databases
- Context engineering
- LLMOps fundamentals:
- Eval harness design
- Prompt versioning
- Production observability
- Cloud-native engineering maturity:
- Kubernetes
- Docker, microservices
- Serverless, CI/CD
- IaC (Terraform or Helm)
- Strong Python; Java (or equivalent) also acceptable
- Production debugging and observability experience
Quality over quantity: A candidate who’s shipped three production agentic systems in four years is preferred over a generalist with passive exposure.
- Experience managing, mentoring, and performance-managing a team of engineers
- Setting individual development plans
- Conducting career conversations
“It took my CV and asked me questions relevant to understanding what kind of jobs to suggest for me. Suggestions were almost perfect. Jobs were exactly what I’ve been looking for.”
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