Build and deploy production-grade generative AI and LLM solutions, including advanced RAG pipelines, prompt strategies, agentic applications, enterprise integrations, and high-throughput unstructured-data processing. Develop with Python and modern ML and LLM frameworks, integrate vector databases, and implement MLOps practices for evaluation, monitoring, CI/CD, and reliable deployment. Collaborate with stakeholders and independently solve ambiguous technical problems while applying GenAI safety guardrails.
Senior GenAI / LLM Engineer (RAG, MLOps)
Role: Senior Generative AI / LLM Engineer
Experience: 6–10 years (Apps Development / Systems Analysis / AI Engineering)
Location: Ireland
Type: Fixed term contract 1 year.
Must‑Have Skills
- Strong foundations in GenAI, ML modeling, Data Science, Statistics, and AI fundamentals (NLP, Neural Networks, LLMs)
- Hands‑on with major LLMs: Google Gemini, OpenAI, Anthropic Claude, Mistral, Llama + open‑source models
- Critical: Deep, hands‑on experience building Retrieval‑Augmented Generation (RAG) pipelines (advanced RAG techniques + implementation)
- Strong Prompt Engineering (prompt strategies, tuning, reusable templates) and agentic frameworks
- Python (mandatory) with strong experience across: Pandas, NumPy, scikit‑learn, PyTorch/TensorFlow, Transformers, FastAPI, LangChain, LlamaIndex
Data / Integration
- Experience integrating GenAI into enterprise apps using APIs, orchestration tools, and knowledge/graph concepts
- Vector DB experience: PGVector, Pinecone, MongoDB Atlas, Neo4j (or similar)
- Experience handling large‑scale unstructured data and high‑throughput processing
Deployment / MLOps
- Critical: Proven experience deploying GenAI/LLM solutions to production
- Strong MLOps knowledge: evaluation, monitoring, and robust deployment pipelines
- CI/CD tools exposure: Jenkins, GitLab CI, Azure DevOps, ArgoCD
- Container orchestration: Kubernetes or OpenShift
Other / Soft Skills
- Strong problem solving, stakeholder collaboration, and ability to work independently on ambiguous problems
- Working knowledge of Guardrails and methods to assess performance/safety of GenAI features
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