Our client, a growing Digital Transformation Consulting organisation, is hiring a hands‑on Senior Applied AI Engineer to join the team in Dublin, Ireland on a contract basis. The successful candidate will design, build and scale advanced Generative and Agentic AI systems, contributing to the development of production-ready agent workflows, sophisticated retrieval pipelines, robust evaluation frameworks and scalable backend services.
Responsibilities
Design, build and operate end‑to‑end production‑grade AI agent systems.
Develop stateful agent workflows with checkpointing, retries and human‑in‑the‑loop controls.
Architect intelligent agents with planning, tool use, memory and escalation strategies.
Implement advanced retrieval pipelines, including hybrid search, reranking and context construction.
Build robust evaluation frameworks with datasets, regression testing and clear success metrics.
Establish LLMOps / AgentOps practices, including observability across cost, latency, drift and failures.
Optimise system performance across latency, cost and output quality (e.g. routing, caching, model selection).
Develop scalable backend services using Python (e.g. FastAPI) and modern architectures.
Deploy and maintain systems using Docker, Kubernetes and CI/CD pipelines.
Translate business requirements into scalable, production‑ready AI solutions.
Skillset
Minimum of 4 years of experience in software engineering, applied machine learning or applied AI.
Strong Python skills, with a solid grounding in modern engineering practices e.g. testing, code quality, version control.
Demonstrated experience developing and deploying LLM‑powered applications, including prompt design, evaluation and productionisation.
Practical experience with agent frameworks such as LangChain, LlamaIndex, LangGraph, CrewAI or Langfuse.
Hands‑on experience with retrieval systems and vector databases (e.g. Milvus, Pinecone, Weaviate, Chroma, FAISS).
Good understanding of AI architecture patterns, including microservices, event‑driven systems and multi‑agent frameworks.
Experience deploying applications on AWS, Azure or GCP using containerisation and CI/CD pipelines.
Strong production mindset, with experience in monitoring, testing, governance and LLMOps practices.
Exposure to developer copilots and rapid prototyping tools (e.g. Cursor, Windsurf, Replit, GitHub Copilot, Claude Code) is a plus.
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