Find Backend software engineer Jobs in Dublin, D

AI Native SW Engineering Consultant
Accenture —Dublin, County Dublin
- Full-time
- Java
- Master's degree
- AI
Engineering Lead
Rain Technologies Inc. —Dublin, County Dublin
- Full-time
- Remote
- System design
- SQL
- AWS
Quick apply
Senior Product Engineer
Intercom —Dublin, County Dublin
- Hybrid work
- AWS
- JavaScript
- No degree
- Work from home
Engineering Manager - Communications Capture
Gong.io —Dublin, County Dublin
- Computer Science
- Java
- Project management
Quick apply
8d
Backend Software Engineer
Grouper —Dublin, County Dublin
- Hybrid work
- Azure
- Computer Science
- Java
Quick apply
Backend Engineer, Communications Capture
Gong.io —Dublin, County Dublin
- Azure
- Java
- AWS
- Annual leave
Quick apply
Senior Software Engineer, Observability
MongoDB —Dublin, County Dublin
- On call
- Azure
- Java
- AWS
Senior Software Engineer (Python)
Telnyx —Dublin, County Dublin
- On call
- Salesforce
- Java
- SQL
Quick apply
Senior Software Engineer, Atlas Clusters
MongoDB —Dublin, County Dublin
- On call
- Azure
- Java
- AWS
Staff Software Engineer, Server Networking Security (Rust)
MongoDB —Dublin, County Dublin
- Azure
- Research
- AWS
Senior Software Engineer, Server Networking Security (Rust)
MongoDB —Dublin, County Dublin
- Azure
- AWS
- Bachelor's degree
Engineering Manager, Server Networking Security (Rust)
MongoDB —Dublin, County Dublin
- Hybrid work
- Azure
- AWS
- Mentoring

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AI Native SW Engineering Consultant

Accenture -
Dublin, County Dublin

Apply Now

Job details

Full-time

Qualifications

CI/CD
Law
Kubernetes
Java
Master's degree
Docker
Terraform
AI
Python
Debugging

Full 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 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

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

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

#LI-EU

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backend software engineer jobs in dublin, d

AI Native SW Engineering Consultant

Engineering Lead

Senior Product Engineer

Engineering Manager - Communications Capture

Backend Software Engineer

Backend Engineer, Communications Capture

Senior Software Engineer, Observability

Senior Software Engineer (Python)

Senior Software Engineer, Atlas Clusters

Staff Software Engineer, Server Networking Security (Rust)

Senior Software Engineer, Server Networking Security (Rust)

Engineering Manager, Server Networking Security (Rust)

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