Senior AI Native Software Engineer
Accenture France
- Paris, France
- Senior Level
- 8+ years
- Posted
Skills
Required
- AI
- observability
- software engineering
- agentic orchestration frameworks
- LangGraph
- CrewAI
- Kubernetes
- Docker
- serverless
- Terraform / Helm
- Python
- Java
- eval
Preferred
- RAG pipeline
- multi-agent orchestration
- lifecycle
- ensuring metric-backed tradeoff decisions
- architecture design sessions
- RAG pipeline ownership
- embeddings
- vector databases
Join our team as a Senior AI Native Software Engineer, where 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 offers a unique opportunity to work across every industry, every enterprise technology stack, and every level of organizational complexity.
People lead responsibilities: experience managing, developing, and performance-managing a team of engineers; setting individual development plans and conducting career conversations
Responsibilities
- Architect and govern production-grade agentic systems at enterprise scale, including multi-agent orchestration and lifecycle observability.
- Define RAG pipeline standards across engagements, establishing chunking and embedding strategies, and ensuring metric-backed tradeoff decisions.
- Lead client engineering engagements at a senior level, facilitating architecture design sessions, leading proof-of-concept delivery, and driving alignment between client technology leadership and delivery teams.
- RAG pipeline ownership: embeddings, chunking strategy, vector databases, and context engineering
Requirements
- 8+ years of software engineering experience in production environments
- Minimum 1 year of 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
- 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
