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At a glance
- Employment type
- Full-time
- Location
- Dublin, Ireland
- Workplace
- On-site
- Category
- AI
Technologies & skills
What you'll be doing
Global investment firm in Dublin seeks a Machine Learning / Gen AI Engineer to design, implement, and improve Gen AI solutions, including RAG, prompt engineering, fine-tuning, ML model deployment, data pipelines, and API development, primarily on AWS. Long-term on-site contract.
Key requirements
Must-have
- 3 to 5 years hands-on development experience in Machine Learning systems
- Experience with Gen AI solutions, Large Language Models, LangChain, Prompt Engineering, Fine Tuning
- Strong programming skills in Python and Java
- Experience developing Python-based APIs (FastAPI or Flask)
- Experience building feature engineering pipelines, deploying AI models, optimizing model inference
- Experience with ML algorithms/frameworks
- Experience with security, data, and AI pipeline technologies (RDS/Postgres, Snowflake, Airflow)
- Experience with infrastructure as code (Terraform, Python, Ansible, CFT)
Nice-to-have
- Experience with search engine platforms like Elasticsearch, Apache SOLR, or OpenSearch
Experience: 3 to 5 years hands-on development experience in Machine Learning systems
Role signals
- Technical focus
- Machine Learning / Gen AI engineering, data pipelines, API development, AWS deployment
- Hands-on vs management
- Hands-on
Similar jobs
Full job description
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Dublin
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Long Term Contract
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Daily Rat
Global investment firm based in Dublin is looking to hire **Machine Learning / Gen AI Engineer ** to design, implement and improve technical solutions for Gen AI solutions including RAG, Prompt Engineering, Fine Tuning, ML model deployment, data pipelines, hosting, and API development. We are looking for an outspoken technologist with a deep passion for data and application design to help build AWS based Gen AI solutions.
The Expertise and Skills You Bring
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3 to 5 years hands on development experience in Machine Learning System.
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Experience with working on Gen AI solutions with Large Language Models, LangChain, LangSmith, LlamaIndex, Prompt Engineering and Fine tuning.
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Strong programming skills in Python, Java.
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Solid experience with developing python-based APIs required (FastAPI / Flask).
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Experience as a Machine Learning engineer building feature engineering pipelines, deploying AI models, and optimizing model inference.* *
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Design and develop machine learning and deep learning systems using appropriate ML algorithms / frameworks.
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Experience working with security, data, and AI pipeline technologies (RDS/Postgres, Snowflake, Airflow) and infrastructure as Code (Terraform, Python, Ansible, CFT).* *
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Demonstrated work experience using AWS services: Deploy the models in Sagemaker, cloud-based data platforms like Snowflake or RDS for analytics data hosting. EC2/EKS compute set-up.
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Work with data to create models and perform statistical analysis, train and retrain models to optimize performance.
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Run machine learning tests and experiments.
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Experience with search engine platforms like Apache SOLR, Elastic Search or OpenSearch would be a big plus.
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Superior SQL skills and experience performing deep data analysis on multiple database platforms.
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Bachelor’s Degree or equivalent experience.
To apply or find out more please reach out to shane.omahony@cpl.ie or 01-947 6301
#LI-SO3
#CplTechnology22
Interview prep pack
Grounded in this listing. Use it to prepare examples before you apply.
Your interview focus
Based on this listing, the role focuses on designing, implementing, and optimizing Gen AI and machine learning solutions, with a strong emphasis on hands-on development, data pipelines, API creation, and AWS-based deployment.
- Hands-on ML/Gen AI engineering·High
- Python API and pipeline development·High
- Cloud deployment (AWS)·Medium
- Infrastructure automation·Medium
Only have 30 minutes?
Follow a focused preparation plan based on this job.
Start 30-minute prep
Your 30-minute plan
Review Gen AI and LLM Project Experience
0–7 minList and summarize your most relevant Gen AI and LLM projects, focusing on tools like LangChain, prompt engineering, and fine-tuning.
Refresh Python API and ML Pipeline Skills
7–14 minPrepare concise examples of building and deploying APIs with FastAPI/Flask and orchestrating ML pipelines with Airflow.
Study AWS ML Deployment Workflows
14–20 minReview your experience with AWS Sagemaker, EC2/EKS, and integrating with data platforms like Snowflake and RDS.
Revisit Infrastructure as Code and Security Practices
20–25 minPrepare to discuss your use of Terraform, Ansible, and security best practices in ML pipelines.
Prepare Questions and Research the Team
25–30 minDraft thoughtful questions for the interview and research the company's AI initiatives if possible.
Talking points
, 6 itemsGen AI Solutions and LLMs
You should be ready to discuss your experience with Large Language Models, LangChain, prompt engineering, and fine-tuning, as these are central to the role.
Python and Java Programming
Demonstrating strong programming skills in both Python and Java is essential, especially for building APIs and ML systems.
API Development with FastAPI/Flask
You will likely need to provide examples of designing and deploying Python-based APIs, as this is a core requirement.
ML Model Deployment and Optimization
Be prepared to explain your process for deploying, optimizing, and monitoring machine learning models, particularly on AWS.
Data Pipeline and Infrastructure as Code
Experience with tools like Airflow, Terraform, and Ansible is important for building robust, scalable ML pipelines.
SQL and Data Analysis
Strong SQL skills and experience with multiple database platforms are required for deep data analysis and feature engineering.
What to research
, 5 itemsGen AI Frameworks and Tools
Review your experience with LangChain, LlamaIndex, and prompt engineering, focusing on practical applications and outcomes.
Python API Development
Prepare to discuss your work with FastAPI or Flask, including design, deployment, and integration with ML models.
AWS ML Services
Refresh your knowledge of deploying ML models using AWS Sagemaker, EC2, EKS, and integrating with data platforms like Snowflake and RDS.
Infrastructure as Code
Be ready to explain your use of Terraform, Ansible, or similar tools for automating ML infrastructure.
SQL and Data Analysis
Review your experience with SQL and performing deep data analysis across multiple database platforms.
Questions to ask
, 6 itemsWhat are the main Gen AI use cases currently being developed by the team?
Why ask this? To understand the practical applications and business impact of your work.
How does the team approach prompt engineering and fine-tuning for LLMs?
Why ask this? To gauge the maturity of Gen AI practices and collaboration opportunities.
What is the typical workflow for deploying ML models to production in your AWS environment?
Why ask this? To clarify deployment processes and expectations for automation and scalability.
How is collaboration structured between data engineers, ML engineers, and software developers?
Why ask this? To assess team dynamics and cross-functional collaboration.
What are the biggest technical challenges the team is currently facing with Gen AI or ML pipelines?
Why ask this? To identify areas where your expertise can add value and understand current pain points.
How does the team ensure data security and compliance, especially when handling sensitive financial data?
Why ask this? To understand the importance of security and compliance in your day-to-day work.
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