AI Native Software Engineer

Accenture | IT services and consulting

2h 36m ago

On-site

Full Time

Lead

France

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 Engineer programme.

#LI-MP

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

Basic Qualifications

Significant years of software engineering experience in production environments
Practical 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

Saint-Herblain

Brest

Egalité des chances face à l'emploi

Chez Accenture, l’inclusion et la diversité sont essentielles à notre culture et font partie intégrante de nos valeurs fondamentales. Nous sommes engagés à créer un environnement de travail dans lesquels tous nos collaborateurs sont respectés et ont le moyen de donner le meilleur d'eux-mêmes. Nous offrons un environnement inclusif, sans égard aux caractéristiques personnelles comme l’origine ethnique, la religion, le genre, l’orientation sexuelle, l’identité ou l’expression du genre, l’âge ou le handicap.

Les candidats à un poste sont tenus de ne pas divulguer les dossiers de condamnation ou d'arrestation sous scellés ou effacés dans le cadre de leur processus d'embauche.

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.

We believe that delivering value requires innovation, and innovation thrives in an inclusive and diverse environment. We actively foster a workplace free from bias, where everyone feels a sense of belonging and is respected and empowered to do their best work.

At Accenture, we see well-being holistically, supporting our people’s physical, mental, and financial health. We also provide opportunities to keep skills relevant through certifications, learning, and diverse work experiences. We’re proud to be consistently recognized as one of the World’s Best Workplaces™.

Join Accenture to work at the heart of change. Visit us at www.accenture.com.

Required Skills

pythonjavalanggraphcrewaiautogenkubernetesdockerterraform

Required Languages

🇬🇧 English

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

Basic Qualifications

Significant years of software engineering experience in production environments

Practical 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