Senior Engineer, Applied AI

Job Description

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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\n

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• Design, build and operate end‑to‑end production‑grade AI agent systems.
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• Develop stateful agent workflows with checkpointing, retries and human‑in‑the‑loop controls.
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• Architect intelligent agents with planning, tool use, memory and escalation strategies.
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• Implement advanced retrieval pipelines, including hybrid search, reranking and context construction.
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• Build robust evaluation frameworks with datasets, regression testing and clear success metrics.
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• Establish LLMOps / AgentOps practices, including observability across cost, latency, drift and failures.
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• Optimise system performance across latency, cost and output quality (e.g. routing, caching, model selection).
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• Develop scalable backend services using Python (e.g. FastAPI) and modern architectures.
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• Deploy and maintain systems using Docker, Kubernetes and CI/CD pipelines.
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• Translate business requirements into scalable, production‑ready AI solutions.
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Skillset\n

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• Minimum of 4 years of experience in software engineering, applied machine learning or applied AI.
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• Strong Python skills, with a solid grounding in modern engineering practices e.g. testing, code quality, version control.
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• Demonstrated experience developing and deploying LLM‑powered applications, including prompt design, evaluation and productionisation.
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• Practical experience with agent frameworks such as LangChain, LlamaIndex, LangGraph, CrewAI or Langfuse.
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• Hands‑on experience with retrieval systems and vector databases (e.g. Milvus, Pinecone, Weaviate, Chroma, FAISS).
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• Good understanding of AI architecture patterns, including microservices, event‑driven systems and multi‑agent frameworks.
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• Experience deploying applications on AWS, Azure or GCP using containerisation and CI/CD pipelines.
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• Strong production mindset, with experience in monitoring, testing, governance and LLMOps practices.
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• Exposure to developer copilots and rapid prototyping tools (e.g. Cursor, Windsurf, Replit, GitHub Copilot, Claude Code) is a plus.
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