Find Lead full stack engineer Jobs in Clonee, Mh

AI Native SW Engineer
Accenture —Dublin, County Dublin
- Full-time
- Java
- Master's degree
- AI
5h
Manager, Security Engineering, Differentiated Security Team
Amazon Data Services Ireland Limited —Dublin, County Dublin
- Full-time
- Computer Science
- Master's degree
- Bachelor's degree
1d
Principal Web Application Architect
Hadfield Green —DUBLIN 2, County Dublin
- Full-time
- Presentation skills
- In-person
Forward Deployed Engineer
Accenture —Dublin, County Dublin
- Full-time
- Presentation skills
- AI
5h
Staff Product Manager
Intercom —Dublin, County Dublin
- Hybrid work
- Annual leave
- Work from home
Business Development Executive
Learnosity —Dublin, County Dublin
- €45,000–€50,000 a year
- Full-time
- Hybrid work
- Computer Science
- Sales
- Salesforce
Quick apply
13d
Lead Sales Engineering Specialist - Security
Fivetran —Dublin, County Dublin
- Full-time
- Remote
- Azure
- Java
- SQL
- Annual leave
- Sick pay
Quick apply
Backend Software Engineer
Grouper —Dublin, County Dublin
- Hybrid work
- Azure
- Computer Science
- Java
Quick apply

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AI Native SW Engineer

Accenture -
Dublin, County Dublin

Apply Now

Job details

Full-time
5 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

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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Refine Your Search

lead full stack engineer jobs in clonee, mh

AI Native SW Engineer

Manager, Security Engineering, Differentiated Security Team

Principal Web Application Architect

Forward Deployed Engineer

Staff Product Manager

Business Development Executive

Lead Sales Engineering Specialist - Security

Backend Software Engineer

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