Senior AI Engineer (Agentic Systems)
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At a glance
What you'll be doing
Senior AI Engineer (Hybrid, Dublin) to design, build, and operate production-grade agentic AI systems in a regulated environment. Hands-on role with ownership of system design, reliability, and delivery. Focus on agent workflows, orchestration, RAG pipelines, and integration with internal platforms.
- Design, build and operate agentic AI systems using Python
- Develop agent workflows using frameworks such as LangGraph or equivalent
- Design and implement context management strategies for multi-step and long-running agent executions
- Build and maintain RAG pipelines with various data sources
- Integrate AI systems with internal platforms via RESTful and event-driven APIs
- Define and implement orchestration patterns for agents, tools, and workflows
Key requirements
Must-have
- Minimum 5 years experience as a software engineer with strong Python skills
- Practical experience with LangGraph or other agentic workflow frameworks
- Deep understanding of context management and MCP-style tool protocols
- Experience with RAG architectures including embeddings and retrieval strategies
- Strong understanding of workflow orchestration concepts
- Strong systems design skills
- Very good communication skills
Nice-to-have
- Experience working with relational and NoSQL databases
- Experience with vector stores
Experience: Minimum 5 years as a software engineer
Role signals
- Technical focus
- AI systems, agentic workflows, orchestration, RAG pipelines
- Architecture / system design
- Indicated in the listing
- Hands-on vs management
- Hands-on
Similar jobs
Full job description
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Dublin – Hybrid
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Long Term Contract
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Daily Rate
We are looking for a senior AI Engineer to work within the Risk Technology organisation. The role focuses on building and operating production-grade agentic systems that support mission-critical workflows in a highly regulated environment. This is a hands-on engineering role with strong ownership of system design, reliability and delivery.
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As an AI Engineer your main responsibilities will involve:
- Design, build and operate agentic AI systems using Python
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Hands-on development of agent workflows using frameworks such as LangGraph or equivalent stateful orchestration approaches
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Design and implementation of context management strategies across multi-step and long-running agent executions
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Build and maintain RAG pipelines leveraging structured, semi-structured and unstructured data sources
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Integrate AI systems with existing internal platforms via RESTful and event-driven APIs
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Define and implement orchestration patterns for agents, tools and workflows (sync, async, human-in-the-loop)
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Must have skills and experience:
- Minimum 5 years experience as a software engineer with strong Python skills
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Practical experience with LangGraph or other agentic workflow frameworks
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Deep understanding of context management and MCP-style tool protocols
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Experience with RAG architectures including embeddings, retrieval strategies etc.
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Strong understanding of workflow orchestration concepts (state, retries, idempotency)
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Strong systems design skills with an ability to reason about scalability and failure modes
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Very good communication skills with the ability to explain complex concepts simply
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Good to have skills and experience:
- Experience working with relational and NoSQL databases and vector stores
To apply or find out more contact shane.omahony@cpl.ie / 01-947 6301
#LI-SO3
#CplTechnology22
Interview prep pack
Grounded in this listing. Use it to prepare examples before you apply.
Your interview focus
Based on this listing, the Senior AI Engineer (Agentic Systems) role centers on designing, building, and operating production-grade agentic AI systems in a regulated environment, with a strong emphasis on Python, workflow orchestration, and RAG architectures.
- Hands-on engineering with agentic AI systems·High
- System design and reliability in regulated environments·High
- Workflow orchestration and context management·High
- RAG pipeline development and data integration·Medium
Only have 30 minutes?
Follow a focused preparation plan based on this job.
Start 30-minute prep
Your 30-minute plan
Review Agentic Workflow Projects
0–8 minSelect and outline 1-2 relevant projects where you designed or operated agentic AI systems, focusing on your technical decisions and outcomes.
Refresh Knowledge of LangGraph and Orchestration Patterns
8–15 minStudy the key features of LangGraph (or similar frameworks) and review orchestration concepts such as state, retries, and idempotency.
Prepare RAG Pipeline Examples
15–21 minSummarize your experience with RAG architectures, including data integration, embeddings, and retrieval strategies.
Practice Explaining Complex Concepts Simply
21–26 minRehearse explaining technical topics (e.g., context management, workflow orchestration) in clear, non-technical language.
Draft Role-Specific Questions
26–30 minWrite down 2-3 thoughtful questions about the team, technology, and processes to ask during the interview.
Talking points
, 6 itemsAgentic Workflow Frameworks (e.g., LangGraph)
Demonstrate your hands-on experience building agent workflows and orchestrating multi-step processes using frameworks like LangGraph.
Context Management Strategies
Show your ability to design and implement strategies for managing context across long-running or multi-step agent executions.
Retrieval-Augmented Generation (RAG) Architectures
Highlight your experience building and maintaining RAG pipelines, including handling various data sources and retrieval strategies.
Workflow Orchestration Concepts
Explain your understanding of orchestration patterns, including state management, retries, idempotency, and human-in-the-loop workflows.
System Design and Reliability
Provide examples of designing scalable, reliable systems and reasoning about failure modes in production environments.
API Integration (RESTful and Event-driven)
Discuss your experience integrating AI systems with internal platforms using RESTful and event-driven APIs.
What to research
, 4 itemsLangGraph or Equivalent Agentic Workflow Frameworks
Review your experience with LangGraph or similar frameworks, focusing on how you designed and implemented agent workflows.
Context Management and MCP-style Tool Protocols
Prepare to discuss strategies for managing context in multi-step agent executions and your familiarity with MCP-style protocols.
RAG Architectures and Data Integration
Refresh your knowledge of RAG pipelines, including handling embeddings, retrieval strategies, and integrating various data sources.
Workflow Orchestration Patterns
Be ready to explain orchestration concepts such as state management, retries, idempotency, and human-in-the-loop workflows.
Questions to ask
, 6 itemsWhat are the main challenges currently faced by the Risk Technology organisation regarding agentic AI systems?
Why ask this? Shows your interest in the team's real-world problems and readiness to contribute.
Which frameworks and tools are most commonly used for agent workflow orchestration in your environment?
Why ask this? Clarifies the technical stack and helps you align your experience.
How does the team ensure reliability and compliance in such a highly regulated environment?
Why ask this? Demonstrates your awareness of regulatory constraints and system reliability.
What is the typical lifecycle of an AI system from design to production in this organisation?
Why ask this? Helps you understand delivery expectations and workflow.
How is collaboration structured between AI engineers and other teams (e.g., data, platform, compliance)?
Why ask this? Assesses cross-functional collaboration and communication expectations.
Are there opportunities to influence the choice of tools or frameworks for future projects?
Why ask this? Shows your interest in contributing to technical decisions and innovation.
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