Find data scientist Jobs in county dublin

AI Architect
Storyful —Dublin, County Dublin
- Full-time
- Azure
- System design
- Java
Machine Learning Systems Engineer
Tether Operations Limited —Dublin, County Dublin
- Full-time
- Remote
- Azure
- Computer Science
- System design
6d
Software Architect - Analytics & Reporting
Optum —Dublin, County Dublin
- Full-time
- Azure
- Master's degree
- AWS
Principal AI Product Manager
Eaton —Dublin, County Dublin
- Computer Science
- Master's degree
- Bachelor's degree
14d
Head of Responsible AI- Vice President
EXL Service —Dublin, County Dublin
- Full-time
- Hybrid work
- Microsoft Excel
- Doctoral degree
- Computer Science
AI Product Manager for Trade & Working Capital Solutions (Vice President)
Citi —Dublin, County Dublin
- €85,600–€128,400 a year
- Full-time
- Hybrid work
- Analysis skills
- No degree
- Product management
AI & Data - Data Governance - Senior Manager
EY —DUBLIN 2, County Dublin
- Full-time
- Hybrid work
- Doctoral degree
- Master's degree
- Bachelor's degree
- Gym membership
- Referral programme
Sr Machine Learning Engineer
paypal —Dublin, County Dublin
- Full-time
- Azure
- Engineering
- Java
- Annual leave
AI Architect (R-18541)
Dun & Bradstreet —Dublin, County Dublin
- Full-time
- Hybrid work
- Azure
- Computer Science
- DevOps
- Annual leave
Quick apply
Head of Digital Platforms and AI Marketing Transformation, Director
Citi —Dublin, County Dublin
- €130,000–€230,000 a year
- Full-time
- Master's degree
- Bachelor's degree
- Product management
Lead AI Engineer - Financial Inclusion
M-KOPA —Dublin, County Dublin
- Full-time
- Remote
- Scripting
- AI
- Leadership
12d
Senior ML Engineer, Computer Vision
Micron Agritech —Dublin, County Dublin
- Full-time
- Hybrid work
- Computer Science
- Master's degree
- SQL
Lead Security Engineer - AI/ML
JPMorganChase —DUBLIN 2, County Dublin
- Full-time
- Master's degree
- Scripting
- In-person
Product Management Leader – AI Portfolio
EPAM Systems, Inc. —Dublin, County Dublin
- Computer Science
- Master's degree
- Bachelor's degree
- Employee stock purchase plan
- Referral programme
Senior Software Engineer - Pricing Analytics
Bloomberg —Dublin, County Dublin
- Computer Science
- Java
- No degree
Senior Engineer, Applied AI
EXL Service —Dublin, County Dublin
- Full-time
- Hybrid work
- Microsoft Excel
- Azure
- Master's degree
Senior Machine Learning Software Development Engineer, AI Ops Integration
Amazon EU SARL, Irish Branch —Dublin, County Dublin
- Full-time
- Computer Science
- Java
- Master's degree
AI & Data - Analytics Delivery - Senior Manager
EY —DUBLIN 2, County Dublin
- Full-time
- Hybrid work
- Azure
- Doctoral degree
- Master's degree
- Gym membership
Head Of Digital Marketing And Analytics
Irish Rugby Football Union (IRFU) —Dublin, County Dublin
- Full-time
- Marketing
- Master's degree
- Analysis skills
- Fuel card
- Annual leave
6d

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AI Architect

Storyful -
Dublin, County Dublin

Apply Now

Job details

Full-time

Qualifications

Elasticsearch
Azure
Law
System design
Google Cloud Platform
Java
Master's degree
AWS
Ruby on Rails
Redshift
DynamoDB
TypeScript
Python

Full job description

Storyful is an equal opportunity employer

Job Description :

Reporting to: Chief Product & Technology Officer (CPTO)

Dublin (Hybrid — 3 days/week in office)

Team: Product & Engineering (foundational hire for Data Science / AI function)
Mission: Build a next-generation Risk & Insights Intelligence Platform that disrupts media monitoring, social listening, and LLM monitoring—from early prototypes to commercially successful, market-leading products.

This role is for someone who can architect and build (hands-on) agentic LLM Systems in production, partner deeply with Data Scientists, and obsess over evaluation, quality, and cost—while thriving in the ambiguity of zero-to-one product creation.

Why this role exists:

We’re building an AI-native platform that detects, explains, and helps teams respond to reputational and narrative risk. You’ll shape the technical direction including network science and explainability early: agent ecosystems, information retrieval (e.g. RAG + Graph RAG), multi-document reasoning, classification, scoring, evaluation, and LLMOps—and turn them into reliable product experiences.

What you’ll do (Responsibilities)

1) Architect and ship agentic GenAI systems

Design and implement agent ecosystems (multi-agent architectures) that deliver real product outcomes (not demos).
Build specialized agents for workflows like adverse media / risk detection, entity investigation, source authenticity, classification, and summarization—and orchestrate them reliably.
Own the translation from research/prototypes into production-grade features (latency, reliability, observability, cost).

2) Build RAG + Graph RAG for multi-doc intelligence

Deliver RAG chatbots for investigation and exploration across large document sets.
Implement multi-document summarization, including Graph RAG patterns (graph extraction, linking entities/claims, narrative threads).
Implement semantic chunking / paragraph splitting, retrieval strategies, and citation/grounding patterns suitable for risk/comms teams.
deep agents or deep research; graph traversal strategies (network science); agentic RAG

3) Multi-document classification + scoring (risk-focused)

Build instruction-based and ML-assisted classification pipelines for multi-document inputs (themes, narratives, risk taxonomy). Explore generating data to fine tune small models.
Create scoring methodologies (e.g., risk score, severity, momentum/growth, confidence, exposure) with a clear rationale and calibration approach.
Bonus: experience building “risk detection” classifiers and adverse media style pipelines.

4) Context engineering + automatic prompt improvement

Lead prompt engineering practices across the product: reusable prompt assets, versioning, guardrails, and domain adaptation.
Implement prompt evolution techniques (e.g., automated prompt iteration / prompt improvement loops) where it makes commercial sense.
Understand the impact of the words in a prompt into the distribution of probabilities the LLM outputs, managing context, through graphs and information retrieval

5) Evaluation: make quality measurable and repeatable

Build robust evaluation methodologies for prompts, RAG, summarization, and classification.
Apply multiple evaluation techniques, including:
- offline metrics (precision/recall/F1 where appropriate)
- retrieval metrics and ablations
- LLM-as-a-judge style evaluations with rubrics, controls, and drift detection
Define quality gates that allow the team to move fast without breaking trust.

Understanding an LLM as a neural network, and not only something that can be prompted and observed from the outside. For example understanding how entropy can be a signal to detect hallucinations while they unfold through the layers of the model.

6) LLMOps + cost control

Implement LLMOps: experiment tracking, model/prompt versioning, dataset management, observability, and release practices.
Build monitoring for quality + safety + cost, and actively optimize infrastructure spend in cloud environments.

Deploying and maintaining open source models

7) Lead by influence (and occasionally by direct leadership)

Bring “Senior/Lead Engineer” judgement: clean architecture, pragmatic decisions, mentoring, unblock teams.
Partner tightly with Product, Design, Data Science, and Engineering—while also being able to execute independently.

What success looks like (first 6–12 months)

A production-grade agentic architecture powering key workflows (investigate summarize classify score recommend action).
A measurable evaluation framework where quality improves release over release.
A Graph RAG (or equivalent) capability that materially improves multi-doc summarization accuracy and defensibility.
Clear cost/performance tradeoffs and observability that make the system operable at scale.
A team around you that’s leveled up in GenAI engineering practices.

Required experience (Must-have)

Proven background as a Senior / Lead Engineer (or equivalent staff-level scope), owning architecture and delivery.
Demonstrated experience building agentic GenAI architecture for commercially successful product features (not only internal prototypes).
Strong experience working with Data Scientists on ML algorithms, NLP, evaluation design, and productionization.
Hands-on experience in AWS and GCP (Azure acceptable as additional).
Production experience with:
- RAG chatbots
- multi-document summarization (ideally Graph RAG)
- multi-document classification
- scoring methodologies (risk scoring is a strong bonus)
Deep expertise in prompt engineering and evaluation, including both classical metrics (e.g., precision/recall) and LLM-as-a-judge approaches.
Strong LLMOps and GenAI product design experience: experimentation deployment monitoring iteration.

Nice-to-have (Strong bonuses)

Experience in risk/compliance domains (e.g., adverse media, AML, entity investigation workflows).
Knowledge graphs in production (e.g., Neo4j) and graph extraction pipelines.
Experience running annotation programs / building labeled datasets for NLP tasks.

Skills & tools (examples)

We don’t require exact matches, but we do expect you to be fluent in this class of tooling and able to choose pragmatically.

GenAI frameworks & LLMs

LangChain, LlamaIndex
OpenAI / Gemini / Claude
Vector RAG + Graph RAG patterns

LLMOps / experimentation / observability

MLflow (experiments, tracking)
Langfuse (prompt & trace observability)

Data & retrieval

Neo4j (graph), ElasticSearch
Vector stores (Pinecone-style capability), embeddings, semantic chunking

Cloud / infrastructure (examples)

AWS: Lambda, SQS/SNS, Kinesis, Glue, Athena, Redshift, DynamoDB, RDS, API Gateway, CloudFront, SageMaker, Comprehend, Kendra, Lex
GCP (plus Azure exposure helpful)

Languages

Python (primary), TypeScript, Java (Ruby on Rails experience welcome)

Job Category:

Storyful - Product & Technology

Apply Now

Refine Your Search

data scientist jobs in county dublin

AI Architect

Machine Learning Systems Engineer

Software Architect - Analytics & Reporting

Principal AI Product Manager

Head of Responsible AI- Vice President

AI Product Manager for Trade & Working Capital Solutions (Vice President)

AI & Data - Data Governance - Senior Manager

Sr Machine Learning Engineer

AI Architect (R-18541)

Head of Digital Platforms and AI Marketing Transformation, Director

Lead AI Engineer - Financial Inclusion

Senior ML Engineer, Computer Vision

Lead Security Engineer - AI/ML

Product Management Leader – AI Portfolio

Senior Software Engineer - Pricing Analytics

Senior Engineer, Applied AI

Senior Machine Learning Software Development Engineer, AI Ops Integration

AI & Data - Analytics Delivery - Senior Manager

Head Of Digital Marketing And Analytics

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