Find Deployed engineer Jobs in Phibsborough, D

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MUFG Investor Services —Dublin, County Dublin
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Quick apply
Quantitative Trader
Virtu Financial —Dublin, County Dublin
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AI/ML Computational Science
Accenture —Dublin, County Dublin
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Sentrium —Dublin, County Dublin
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Amazon Development Centre Ireland Limited —Dublin, County Dublin
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Anthropic —Dublin, County Dublin
- €200,000–€255,000 a year
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ElevenLabs —Dublin, County Dublin
- Full-time
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Grandstand —Dublin, County Dublin
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3d
Director of AI Engineering
Mastercard —Dublin, County Dublin
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Virtu Financial —Dublin, County Dublin
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Spanish Point Technologies —Clontarf, County Dublin
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AI Native SW Engineer - Manager

Accenture -
Dublin, County Dublin

Apply Now

Job details

Full-time
2 days 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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Refine Your Search

deployed engineer jobs in phibsborough, d

AI Native SW Engineer - Manager

Principal Cloud & Kubernetes Engineer

Senior Product Engineer

Senior Software Security Engineer

Architecture Director - Data Centres

Senior AI Engineer

Practice Lead - Data Science_ ML

Private Markets SME, NAV Production (Fund Accounting and Administration)

Quantitative Trader

AI/ML Computational Science

Quantitative Trader - ETFs

NEC4 ECC Project Manager

Development Manager

Sr. Technical Program Manager, Network Monitoring and Remediation

AI Compliance Officer

Engineering - Internal AI Transformation

Commercial Operations Engineer

Director of AI Engineering

Quantitative Trader - Fixed Income

Data Analytics and AI Team Lead

I want to receive the latest job alerts for deployed engineer in phibsborough, d

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