# Confidential — Senior AI Engineer, SRE & LLM Infrastructure

| Field | Value |
|---|---|
| **Date found** | 2026-05-29 |
| **Company** | Confidential (client undisclosed) |
| **Role** | Senior AI Engineer, SRE & LLM Infrastructure |
| **Location** | Greater Dublin — Hybrid |
| **Salary** | Undisclosed |
| **Job URL** | https://www.linkedin.com/jobs/view/4422001654/ |
| **Status** | New |

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## Company Research

Recruiting agency — client undisclosed.

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## Job Summary

**What they do:** Established enterprise software company with LLM-powered capabilities in production serving real paying customers.

**The role:** Senior SRE/production engineer owning reliability, uptime, and scalability of LLM serving infrastructure — GPUs, Kubernetes, SLOs, and incident response applied to large language models.

**Core work:**
- Own reliability and scalability of LLM serving in production — uptime, latency percentiles, cost per token
- Operate AI infrastructure on GPUs + Kubernetes with SRE discipline (capacity planning, autoscaling, blast-radius control)
- Harden serving stacks (vLLM/TensorRT-LLM/Triton) against traffic spikes and GPU operational issues

**Stack:** Python · vLLM · TensorRT-LLM · Triton · NVIDIA GPUs · Kubernetes · Cloud (AWS/GCP/Azure) · SLO/observability tooling

**Work style:** Dublin Hybrid — frequency unknown; requires Dublin work authorisation; client identity undisclosed

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## Score: 53%

| Dimension | Score | Justification |
|---|---|---|
| Agentic AI depth (25%) | 30% | SRE / infrastructure role for LLM serving — keeping models running, not building agentic systems |
| Tech fit (25%) | 65% | LLM serving stacks, Kubernetes, cloud infrastructure — technically adjacent but not the core target stack |
| Remote fit (25%) | 50% | Dublin Hybrid with unknown frequency |
| Company culture fit (15%) | 55% | Unknown client described as "enterprise software company" — likely not AI-native startup |
| IC/leadership balance (10%) | 85% | IC SRE role with some potential for senior/lead influence |
| **Final (weighted)** | **53%** | |

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## Strengths

- Fresh Dublin posting (today) — very early applicant window (only 2 applicants at time of discovery)
- Deep LLM infrastructure work is in demand; good exposure to production AI systems at scale
- Bologna alumni connection visible in the job posting

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## Weaknesses & Risks

- Client is completely anonymous — culture and actual company unknown
- SRE role, not agentic AI engineering — far from the target of multi-agent pipeline work
- Requires GPU/CUDA infrastructure expertise and on-call rotation
- Hybrid Dublin with unknown frequency is a risk

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## Suggestions

- Only apply if interested in production AI infrastructure (LLM serving, reliability engineering)
- Ask to reveal the client company before investing time in interviews
- Emphasise Kubernetes and distributed systems experience

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## Interview Tracker

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