Senior Applied AI Engineer – Gen & Agentic AI
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At a glance
Technologies & skills
What you'll be doing
Join a global AI Innovation and R&D team as a Senior Applied AI Engineer, focusing on building production-ready Generative and Agentic AI systems. This hands-on role involves designing, building, and operating advanced AI systems with an emphasis on reliability, scalability, and production readiness.
- Design, build, and own production-grade agentic AI systems end-to-end
- Develop stateful and reliable agent workflows, including planning, tool usage, memory, and human-in-the-loop controls
- Implement advanced retrieval and grounding techniques such as hybrid search, reranking, and contextual assembly
- Build secure tool integrations and expose internal and external services for agent consumption
- Treat AI evaluation as an engineering discipline using regression testing, metrics, and release gates
- Implement LLMOps and AgentOps practices to monitor cost, latency, quality, and system performance
- Optimise AI systems for performance, reliability, and cost efficiency across models and providers
- Build and operate backend services, including Python APIs, workflow orchestration, and event-driven systems
Key requirements
Must-have
- 6+ years of experience in software engineering, applied machine learning, or applied AI
- Strong Python development skills with solid software engineering fundamentals
- Proven experience building and deploying LLM-powered applications in production
- Hands-on experience with agentic frameworks such as LangChain, LlamaIndex, LangGraph, CrewAI, or similar
- Strong understanding of retrieval systems and vector databases
- Familiarity with agent design patterns including planning, tool use, ReAct, and multi-agent systems
- Experience deploying and operating production systems on cloud platforms such as AWS, Azure, or GCP
- Comfortable working with Docker, CI/CD pipelines, and production environments
Experience: 6+ years
Role signals
- Technical focus
- AI systems, agentic workflows, production engineering
- Architecture / system design
- Indicated in the listing
- Hands-on vs management
- Hands-on
Similar jobs
Full job description
Senior Applied AI Engineer – Generative & Agentic AI
- €100-112K + Bonus + Bens
- An exciting opportunity for a Senior Applied AI Engineer to join a global AI Innovation and R&D team focused on building production-ready Generative and Agentic AI systems.
- This is a hands-on engineering role suited to someone who enjoys delivering real-world AI products and working at the intersection of software engineering, agentic workflows, and applied GenAI.
The Role
You will design, build, and operate advanced AI systems end-to-end, with a strong focus on reliability, scalability, and production readiness.
- Design, build, and own production-grade agentic AI systems end-to-end
- Develop stateful and reliable agent workflows, including planning, tool usage, memory, and human-in-the-loop controls
- Implement advanced retrieval and grounding techniques such as hybrid search, reranking, and contextual assembly
- Build secure tool integrations and expose internal and external services for agent consumption
- Treat AI evaluation as an engineering discipline using regression testing, metrics, and release gates
- Implement LLMOps and AgentOps practices to monitor cost, latency, quality, and system performance
- Optimise AI systems for performance, reliability, and cost efficiency across models and providers
- Build and operate backend services, including Python APIs, workflow orchestration, and event-driven systems
- Collaborate with data science, backend, and delivery teams to deliver scalable AI solutions
- Contribute to reusable components, technical documentation, and ongoing AI innovation initiatives
Experience & Skills
- 6+ years of experience in software engineering, applied machine learning, or applied AI
- Strong Python development skills with solid software engineering fundamentals
- Proven experience building and deploying LLM-powered applications in production
- Hands-on experience with agentic frameworks such as LangChain, LlamaIndex, LangGraph, CrewAI, or similar
- Strong understanding of retrieval systems and vector databases
- Familiarity with agent design patterns including planning, tool use, ReAct, and multi-agent systems
- Experience deploying and operating production systems on cloud platforms such as AWS, Azure, or GCP
- Comfortable working with Docker, CI/CD pipelines, and production environments
- Strong delivery mindset with a focus on building high-quality, reliable AI systems
- Must have Irish/EU Citizenship and be based within a commutable distance to Dublin
#LI-JM2
Interview prep pack
Grounded in this listing. Use it to prepare examples before you apply.
Your interview focus
Based on this listing, the Senior Applied AI Engineer role focuses on designing, building, and operating production-ready generative and agentic AI systems, with an emphasis on reliability, scalability, and hands-on engineering using Python and modern AI frameworks.
- Hands-on agentic AI engineering·High
- Production deployment and reliability·High
- Retrieval and workflow orchestration·Medium
- Cloud and DevOps proficiency·Medium
Only have 30 minutes?
Follow a focused preparation plan based on this job.
Start 30-minute prep
Your 30-minute plan
Review Recent Agentic AI Projects
0–8 minSelect 1-2 recent projects where you designed or deployed agentic AI systems and prepare concise summaries highlighting your role, challenges, and outcomes.
Refresh Knowledge of Retrieval and Vector Databases
8–14 minRevisit your experience with retrieval-augmented generation, hybrid search, and vector database integration, focusing on technical details and impact.
Summarize LLMOps/AgentOps Practices
14–20 minList the tools, metrics, and processes you have used for monitoring, regression testing, and optimizing AI systems in production.
Prepare Cloud Deployment and DevOps Examples
20–25 minGather examples of deploying AI systems on AWS, Azure, or GCP, including use of Docker and CI/CD pipelines.
Draft Targeted Questions for the Interviewer
25–30 minSelect and tailor 2-3 questions from the provided list to ask during your interview, based on your interests and the role's focus.
Talking points
, 5 itemsEnd-to-End Agentic AI System Design
You should be able to discuss your experience architecting and delivering agentic AI systems, highlighting your approach to reliability, scalability, and production readiness.
Hands-on Experience with Agentic Frameworks
Prepare to give concrete examples of using frameworks like LangChain, LlamaIndex, or similar, focusing on how you implemented agent workflows and integrated tools.
Retrieval Systems and Vector Databases
Demonstrate your understanding of retrieval-augmented generation, hybrid search, and how you have used vector databases to improve AI system performance.
LLMOps and AgentOps Practices
Be ready to explain how you monitor, evaluate, and optimize AI systems for cost, latency, and quality, including your use of regression testing and release gates.
Cloud Deployment and DevOps
Showcase your experience deploying and operating AI systems on AWS, Azure, or GCP, and your proficiency with Docker and CI/CD pipelines.
What to research
, 4 itemsAgentic AI Frameworks (LangChain, LlamaIndex, etc.)
Review your hands-on experience with agentic frameworks, focusing on workflow design, tool integration, and production deployment.
Retrieval Systems and Vector Databases
Prepare to discuss your use of retrieval-augmented generation, hybrid search, reranking, and vector database integration in real projects.
Cloud Deployment and DevOps Practices
Refresh your knowledge of deploying AI systems on AWS, Azure, or GCP, including Docker, CI/CD, and monitoring tools.
LLMOps and AgentOps Methodologies
Be ready to explain your approach to AI system evaluation, regression testing, and operational monitoring for cost, latency, and quality.
Questions to ask
, 6 itemsWhat are the main challenges your team faces when deploying agentic AI systems in production?
Why ask this? To understand the technical and operational pain points and how your experience can address them.
How does the team approach evaluation and monitoring of AI system performance and reliability?
Why ask this? To gauge the maturity of LLMOps/AgentOps practices and your potential contributions.
What frameworks and tools are most commonly used for agent workflow orchestration here?
Why ask this? To clarify the technology stack and align your expertise with their practices.
How do you ensure secure integration of internal and external tools for agent consumption?
Why ask this? To assess the importance of security and compliance in the team's workflow.
What opportunities exist for contributing to AI innovation and reusable components within the team?
Why ask this? To explore how you can participate in ongoing R&D and technical leadership.
How is collaboration structured between AI engineering, data science, and delivery teams?
Why ask this? To understand cross-functional workflows and communication expectations.
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