# Citco — Senior AI Engineer

| Field | Value |
|---|---|
| **Date found** | 2026-05-26 |
| **Company** | Citco |
| **Role** | Senior AI Engineer |
| **Location** | Dublin, Ireland |
| **Salary** | Undisclosed |
| **Job URL** | https://to.indeed.com/aaywgsvbyvh4 |
| **Status** | New |

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## Company Research

| Field | Value |
|---|---|
| **Headquarters** | Amsterdam, Netherlands |
| **Founded** | 1948 |
| **Employees** | ~8,000+ |
| **LinkedIn** | linkedin.com/company/citco |
| **Website** | https://www.citco.com |
| **Blog** | — |

- **Product:** Fund administration, custody, and financial services technology for the alternative investments industry (hedge funds, private equity, real assets).
- **Customers:** Global alternative investment managers, hedge funds, and private equity firms.
- **Notable:** One of the world's largest independent fund administrators with ~$2T+ AuA; Dublin is a major technology and operations hub; proprietary software development is core to their differentiation strategy.

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## Job Summary

**What they do:** Citco provides fund administration and proprietary technology platforms to the alternative investment industry, with Dublin hosting major engineering teams.

**The role:** Senior AI Engineer on the Innovation IT team, designing and implementing production agentic AI systems (using AWS Strands framework) and MLOps/LLMOps tooling.

**Core work:**
- Architect and build agentic AI applications following Agentic AI design patterns, AgentOps, and agent validation/evaluation (AWS Strands framework)
- Design and maintain enterprise MLOps/LLMOps platform on AWS; deploy and monitor production ML systems
- Research and implement cutting-edge AI/ML techniques for proprietary financial services applications

**Stack:** Python · AWS · AWS Strands · LLMs · MLOps · LLMOps · AgentOps · Deep Learning · TensorFlow/sklearn

**Work style:** Dublin office; work arrangement not stated — likely hybrid. "Global team environment" mentioned; "global development centres" network.

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## Score: 58%

| Dimension | Score | Justification |
|---|---|---|
| Agentic AI depth (25%) | 72% | Explicitly agentic AI with production agent design patterns, AgentOps, evaluation — solid depth using AWS Strands |
| Tech fit (25%) | 62% | Python + AWS strong match; uses AWS Strands (not LangChain/LangGraph) — framework gap but same patterns |
| Remote fit (25%) | 45% | Dublin office, no explicit remote policy; "Innovation IT" core team implies regular in-office presence; hybrid frequency unknown |
| Company culture fit (15%) | 35% | Large traditional financial services firm (~8,000 employees, founded 1948) — not AI-native; likely heavy compliance and process |
| IC/leadership balance (10%) | 72% | Primarily IC engineering with "lead in concept design" and participation in demos; no management responsibilities |
| **Final (weighted)** | **58%** | |

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## Strengths

- Genuine agentic AI focus with production requirements (AgentOps, validation, evaluation) — not just LLM integration
- AWS stack alignment strong; Luca's AWS/MLOps experience transfers directly
- 8+ years Python requirement matches Luca's seniority; "enterprise MLOps/LLMOps platform design" maps to his production experience
- Alternative investments / financial compliance domain has overlap with Fenergo KYC/AML regulatory work

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## Weaknesses & Risks

- Uses AWS Strands rather than LangChain/LangGraph — framework is newer and less widely known
- Work arrangement not disclosed; large financial services company makes fully remote unlikely
- Salary undisclosed; Citco may not reach €110k+ for this level in Dublin
- Culture likely conservative and process-heavy given scale and regulated nature of business

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## Suggestions

- Verify work arrangement (remote/hybrid/office) before investing time in application
- Position Fenergo agentic document pipeline as directly relevant — regulatory document automation → fund administration automation
- Emphasise AWS experience prominently; note comfort picking up AWS Strands given deep LangGraph/agent framework background
- Ask: what does day-to-day hybrid look like for Innovation IT? How much greenfield vs. maintenance?

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## Interview Tracker

| Stage | Date | Notes |
|---|---|---|
| Applied | | |
| Recruiter screen | | |
| Technical interview | | |
| Final round | | |
| Offer / Outcome | | |