Find Stack software engineer team lead, charles river development, assistant vice Jobs in Dublin 12, D

AI Native SW Engineer - Manager
Accenture —Dublin, County Dublin
- Full-time
- Java
- Master's degree
- AI
17h
Operations Technician
Charles River Laboratories —Dublin, County Dublin
- Full-time
- Sales
- Microsoft Office
- No degree
- Annual leave
Controls Business Partner
Barclays —Dublin, County Dublin
- From €56,000 a year
Staff Engineer
Seapoint —Dublin, County Dublin
- Full-time
- Hybrid work
- System design
- AWS
Quick apply
Staff Product Manager
Intercom —Dublin, County Dublin
- Hybrid work
- Annual leave
- Work from home
Backend Software Engineer
Grouper —Dublin, County Dublin
- Hybrid work
- Azure
- Computer Science
- Java
Quick apply
Data Engineer
Grouper —Dublin, County Dublin
- Full-time
- Hybrid work
- Azure
- Computer Science
- SQL
Quick apply
Cloud Platform Engineer
Grouper —Dublin, County Dublin
- Full-time
- Remote
- Azure
- Java
- AWS
Quick apply
Engineering Manager, Server Networking Security (Rust)
MongoDB —Dublin, County Dublin
- Hybrid work
- Azure
- AWS
- Mentoring
Staff Engineer
MongoDB —Dublin, County Dublin
- On call
- Azure
- Java
- AWS
Director of Software Engineering - Workplace Technology
JPMorganChase —DUBLIN 2, County Dublin
- Full-time
- Azure
- Computer Science
- Java
2h
Linux/Embedded Firmware Engineer
Full Stack Energy —Dublin, County Dublin
- Full-time
- Hybrid work
- Computer Science
- System design
- English
Quick apply
1h

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AI Native SW Engineer - Manager

Accenture -
Dublin, County Dublin

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Job details

Full-time
17 hours ago

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

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

Extensive experience in software engineering 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

People lead responsibilities: experience managing, developing, and performance-managing a team of engineers; setting individual development plans and conducting career conversations

#LI-EU

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stack software engineer team lead, charles river development, assistant vice jobs in dublin 12, d

AI Native SW Engineer - Manager

Operations Technician

Controls Business Partner

Staff Engineer

Staff Product Manager

Backend Software Engineer

Data Engineer

Cloud Platform Engineer

Engineering Manager, Server Networking Security (Rust)

Staff Engineer

Director of Software Engineering - Workplace Technology

Linux/Embedded Firmware Engineer

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