Accenture
AI Native Engineer (Agentic / Applied)
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AI Engineer (Agentic/App Applied) – Accenture
About the Role
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’ve shipped a multi-agent system in production.
- You’ve owned the eval harness to diagnose failures.
- You know what happens when your agent fails at 2AM—because you’ve experienced 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 across 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. At Accenture, we offer what no single product company can:
- Breadth across every industry, every enterprise technology stack, and every level of organizational complexity.
- Vendor fellowship access inside Anthropic, OpenAI, Microsoft, and Google engineering teams, plus a direct pathway to the Forward Deployed Engineer programme (deployment at client sites globally).
Key Responsibilities
- Architecture & Governance
- 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, 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 from the outset:
- Vendor-agnostic architecture with fallback routing and cost governance.
- Integration with OpenAI, Anthropic, Vertex AI, and open-source models.
- Architect and govern production-grade agentic systems at enterprise scale:
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LLMOps at Scale
- Own LLMOps across multiple concurrent systems:
- Eval strategy, prompt governance, observability tooling standards.
- Safety monitoring and cost controls.
- Lead client engineering engagements:
- Facilitate architecture design sessions.
- Lead proof-of-concept delivery.
- Drive alignment between client technology leadership and delivery teams.
- Own LLMOps across multiple concurrent systems:
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Reusable Patterns & Industry Standards
- Shape and publish reusable patterns, accelerators, and engineering standards to 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 stakeholders.
Basic Qualifications
- Software engineering experience in production environments.
- Hands-on experience designing and deploying agentic AI solutions in production (critical—no excuses).
- Experience with agentic orchestration frameworks at production depth (e.g., LangGraph, CrewAI, AutoGen—don’t fake familiarity).
- Direct experience calling LLM APIs in production code:
- Provider abstraction.
- Token management, latency, and cost tradeoffs (Optimization roadmaps = ticketentum).
- Ownership of RAG pipelines:
- Embeddings, chunking strategy, vector databases, and context engineering.
- LLMOps fundamentals:
- Eval harness design, prompt versioning, and production observability.
- Cloud-native maturity:
- Kubernetes, Docker, microservices, serverless, CI/CD, and infrastructure as code (Terraform or Helm).
- Strong knowledge of Python; Java or equivalent backend language acceptable.
- Debugging and observability experience where QOE is weighted over buzzwords.
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Preferred over generalists:
- Candidates who’ve shipped three production agentic systems in four years would be rolls in one vs. a “AI-generalist” unburdened by dilemmas.
People Leadership
- Experience managing, developing, and performance-managing teams of engineers.
- Setting individual development plans and conducting career conversations.
About Accenture
Accenture is a global leader in professional services, helping businesses, governments, and organizations build their digital core, optimize operations, and harness the power of AI.
At the core of change today, we deliver through: ✔ Strong ecosystem relationships in AI, data, and cloud. ✔ Unmatched industry experience, functional expertise, and global delivery.
We combine these strengths to help clients reinvent. Our mission is 360° value creation—generating impact for clients, employees, shareholders, partners, and communities.
Wherever you sit (Strategy & Consulting, Technology, or Industry X), we align ambitions with real-world results. Diversity fuels our innovation, and we hire more for grit than graduations, celebrating those who thrive by working smart, working together.
Equal Opportunity & Belonging
At Accenture, no one should be discriminated against because of differences. We embrace and celebrate range, with alternatives for every identity we don’t mandate.
Bring the unusual talent, urge your unusual ambition, and advance with us—you won’t just blend in.
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