AI Native SW Engineer - Manager / Director

Accenture; Greater London; Híbrido; GBP 90 000 - 130 000; Tempo integral

Resumo da oferta

Accenture is seeking an experienced AI Engineer (Agentic/Applied) to design, build, and deploy production-grade agentic AI systems across enterprise technology stacks. You will lead design sessions with client engineering teams, develop reusable patterns, and drive architecture decisions for scalable AI deployments.

The role requires hands-on experience with production agentic AI solutions, multi-LLM integrations, and strong Python/JVM skills, with a focus on reliability, observability, and cost

Qualificações

• Extensive software engineering experience in production environments.
• Hands-on experience designing and deploying agentic AI solutions in production environments.
• Demonstrated experience with agentic orchestration frameworks: LangGraph, CrewAI, AutoGen, or equivalent in production depth.
• Direct experience calling LLM APIs (OpenAI, Anthropic, Vertex AI) in production code.
• RAG pipeline ownership: embeddings, chunking strategy, vector databases, and 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 backend language; production debugging and observability.
• Quality of experience prioritized; production agentic systems preferred over generalist exposure.
• People lead responsibilities: team development and performance management.

Responsabilidades

• Architect and govern production-grade agentic systems at enterprise scale, including multi-agent orchestration and lifecycle observability.
• Define RAG pipeline standards across engagements, including chunking and embedding strategies and metric-backed decisions.
• Set multi-LLM integration standards with vendor-agnostic architecture and cost governance.
• Own LLMOps at programme scale: eval strategy, prompt governance, observability, and cost controls.
• Lead client engineering engagements at senior level, guiding architecture sessions and delivery of PoCs.
• Publish reusable patterns, accelerators, and engineering standards across the practice.
• Define and present program-level AI impact to senior client stakeholders with clear metrics.

Conhecimentos

Python

LLMOps

Kubernetes

Docker

Cloud-native

Production debugging

Lead engineers

Communication

Ferramentas

LangGraph

CrewAI

AutoGen

OpenAI API

Vertex AI

Descrição da oferta de emprego

Job Description

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.

As an AI Engineer (Agentic/Applied), you will design, build, and deploy production-grade agentic AI systems 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.

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 Engineerprogramme.

Key Responsibilities

• Architect and govern production-grade agentic systems 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: eval strategy, prompt governance, observability tooling standards, safety monitoring and cost controls across multiple concurrent systems

• Lead client engineering engagements at senior level - 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

Qualification

• 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

Locations

London

Birmingham

Equal Employment Opportunity Statement

All employment decisions shall be made without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status or any other basis as protected by federal, state, or local law.

Job candidates will not be obligated to disclose sealed or expunged records of conviction or arrest as part of the hiring process.

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About Accenture

We work with one shared purpose: to deliver on the promise of technology and human ingenuity. Every day, more than 775,000 of us help our stakeholders continuously reinvent. Together, we drive positive change and deliver value to our clients, partners, shareholders, communities, and each other.

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