Applied AI Engineer (all genders)

Accenture DACH; Zürich; Hybrid; CHF 120,000 - 180,000; Full time

Benefits offered by this job

Hybrid/Remote work options

Mentoring and structured onboarding

Diversity and inclusion culture

Client-facing collaboration

Job summary

Accenture DACH is seeking an AI Engineer (Agentic/Applied) to design, build, and deploy production-grade agentic AI systems across the full enterprise technology stack in Zürich. You will collaborate with client engineering teams, lead technical design sessions, and create reusable patterns that scale beyond a single project.

The role requires extensive production experience, hands-on deployment of agentic AI solutions, and strong knowledge of LLM providers (OpenAI, Anthropic, Vertex AI).

Qualifications

• Production environments experience
• Hands-on production deployment of agentic AI systems
• Experience with agentic orchestration frameworks at production depth
• Experience calling LLM APIs in production code
• RAG pipeline ownership including embeddings and vector databases
• LLMOps with eval harnesses and prompt versioning
• Cloud-native engineering: Kubernetes, Docker, microservices, serverless, CI/CD, IaC
• Strong Python or Java backend
• Fluent German and English; travel readiness

Responsibilities

• Design and build production-grade agentic systems end-to-end: multi-agent orchestration, RAG pipelines, policy-based routing, tool invocation, memory management, observability
• Build and own RAG pipelines: embeddings, chunking, vector search, context window engineering
• Integrate across LLM providers with fallback routing, token and cost management
• Implement LLMOps in production: eval harnesses, prompt versioning, observability tooling, cost/safety monitoring
• Collaborate with client engineering teams through workshops and architecture walkthroughs
• Create reusable patterns and playbooks to scale across engagements
• Define metrics for agent accuracy, latency, safety, and cost-effectiveness; communicate with stakeholders
• Minimum of extensive software engineering experience in production environments
• Direct production experience deploying agentic AI solutions
• Experience with LangGraph, CrewAI, AutoGen or equivalent at production depth
• Direct production calls to OpenAI, Anthropic, Vertex AI APIs with provider abstraction and latency management
• RAG ownership: embeddings, chunking, vector databases, context engineering
• LLMOps fundamentals: eval harness design, prompt versioning, observability
• Cloud-native: Kubernetes, Docker, microservices, serverless, CI/CD, IaC
• Strong Python; Java or equivalent backend language
• Preference for three production agentic systems shipped in four years
• German and English with willingness to travel

Skills

Production software engineering

Agentic AI solutions

LLM orchestration frameworks

LLM API usage

RAG pipelines

LLMOps

Kubernetes

Docker

CI/CD

IaC

Python

Java

German language

English language

Education

Tools

Docker

Kubernetes

Terraform

Helm

LangGraph

CrewAI

AutoGen

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 Engineer programme.

Key Responsibilities

• Design and build production-grade agentic systems end-to-end: multi-agent orchestration, RAG pipelines, policy-based routing, tool invocation, memory management, and lifecycle observability;
• Build and own RAG pipelines: embeddings, chunking strategy, vector search, context window engineering and tuning against real quality targets;
• Integrate and abstract across multiple LLM providers — OpenAI, Anthropic, Vertex AI, and open-source models — with fallback routing, token, cost, and latency management;
• Implement LLMOps in production: eval harnesses with real quality metrics, prompt versioning, observability tooling (LangSmith, Braintrust, or equivalent), cost and safety monitoring;
• Embed directly with client engineering teams to design, prototype, and deploy agentic solutions — workshops, proofs of concept, code-with sessions, and architecture walkthroughs;
• Build reusable patterns, accelerators, and playbooks that scale beyond the individual client engagement and enable the next one to start faster;
• Define and use metrics to measure agent accuracy, latency, safety, and cost-effectiveness; present findings and recommendations to client stakeholders in business terms.
• Extensive years of 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.
• Very good German and English language skills, as well as a willingness to travel occasionally for business purposes.

What We Offer

• A workplace culture that celebrates diversity, inclusion, and a sense of belonging.
• Extensive learning and development opportunities to help you continuously build your skills and advance your career.
• Dedicated mentoring and structured onboarding to support your professional growth from day one.
• The flexibility to work where you can make the greatest impact: from an Accenture office, remotely, or directly with clients.

The Accenture Culture: Here, YOU are the catalyst for change

Your skills, personality, and ambitions make you unique. And when diverse talents, strengths, and perspectives come together, something extraordinary happens: ideas that create real impact in the world. That's why, at Accenture, we champion diversity and individual growth. Create a work environment where you can thrive. Take on challenges that matter to you. Shape a workload that fits your life. Work in ways that support your goals. How you connect, learn, and grow is up to you. This is your career. We're here to help you build it the way you envision it.