Role overview
- Programming ML: Python, FastAPI, Transformers, Scikit-learn , deep learning libraries (TensorFlow, Keras, PyTorch, etc.) and modern agentic frameworks (e.g., LangChain, LlamaIndex).
- LLMs: Open-source LLMs, Claude, GPT-5
- NLP Models: BERT, RoBERTa and related architectures
- Search Retrieval: Elasticsearch, Reranking Models, RAG pipelines
- Data: DuckDB, MS SQL Server, Databricks
- Deployment: Docker, Azure (CI/CD pipelines, Container Apps, cloud services)
- Assist in designing, developing, and improving AI and ML models for AP automation use cases
- Build and evaluate NLP and document-understanding models (OCR, extraction, classification, matching)
- Support development of GenAI and RAG-based features, including chatbots and analytics assistants
- Troubleshoot, debug, and optimize complex AI systems to ensure optimal performance, reliability, and scalability in production environments.
- Evaluation Observability: Implementing LLM evaluation frameworks and monitoring for latency, accuracy, and tool usage.
- Perform data analysis to identify patterns, errors, and improvement opportunities • Work with domain experts to translate business problems into AI solutions
- Contribute to code quality through documentation, testing, and peer reviews
- Stay up to date with emerging AI/ML and GenAI trends and apply relevant learnings
Basic qualifications
- Degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or a related quantitative field
- 0–2 years of experience (or strong academic/project experience) working with ML or AI systems
- Strong Python programming skills
- Strong understanding of machine learning concepts such as model training, evaluation, and feature engineering
- Exposure to NLP, transformers, or LLM-based systems (academic or practical)
- Basic understanding of SQL and working with structured data
- Strong problem-solving mindset and willingness to learn
- Deep understanding of foundational ML concepts (gradient descent, model training, attention, embeddings, transfer learning).
- Exposure to AI Agents, RAG architectures, or chatbots
- Familiarity with Docker and cloud platforms (Azure preferred)
- Experience with Spark / PySpark or Databricks
- Experience with data visualisation tools such as Matplotlib, Plotly, or similar
Benefits
- Hybrid working model (Dublin office + remote)
- Mentorship from experienced AI and engineering professionals
- Opportunity to work on production AI systems used by real customers
- Exposure to cutting-edge AI, LLMs, and Agentic AI use cases
- A collaborative, learning-focused environment with real ownership
About the company
- Passion Pride
- Security Trust
- Initiative Ownership
Tags & focus areas
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