Remote AI Platform Engineer: Build Autonomous Agents

Jobgether

Ireland

Remote

EUR 120,000 - 180,000

Full time

2 days ago

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Benefits offered by this job

Remote-first

Health insurance

Home office allowance

Professional development stipend

Connectivity reimbursement

Flexible time off

Laptop provided

Well-being resources

Job summary

Jobgether seeks an experienced AI Platform Engineer based in Ireland to join a remote-first team building the AI platform powering autonomous agents across business functions. You will design, develop, and operate infrastructure enabling safe and reliable AI tasks, collaborating across runtimes, identity management, security guardrails, and observability.

You will productionize AI workflows, deploy MCP servers, and develop autonomous agents with tool integrations.

Qualifications

At least 5 years of experience as a software, platform, or infrastructure engineer.
Between 6 and 12 months of shipping LLM-based systems to production and supporting agentic workflows.
Daily hands-on experience using AI coding agents such as Claude Code and Codex to write production code.
Experience developing autonomous or multi-step agents with tool integrations and relevant SDKs/APIs.
Practical experience building MCP servers, including OAuth authentication flows and scoped tokens.
Knowledge of identity management systems (Okta or similar) including SSO, SCIM, groups.
Experience creating automated system identities and managing secrets.
Understanding of security guardrails, audit logs, CI checks, and policy-as-code.
Experience designing evaluation systems for LLM apps with test sets and CI gates.
Observability experience with tracing, metrics, and dashboards (OpenTelemetry, Grafana, Datadog).
Experience monitoring AI expenditure and cost attribution.
Strong programming skills in TS/Python/Go and CI/CD knowledge.
Ability to automate processes with measurable impact.

Responsibilities

Productionizing AI workflows and turning prototypes into robust production systems with SLAs.
Designing, deploying, securing, and maintaining MCP servers with per-user OAuth.
Developing autonomous agents capable of multi-step actions across tools.
Establishing platform-wide security guardrails and least-privilege access policies.
Creating evaluation frameworks with test datasets and live production scoring.
Improving observability with tracing, logging, latency and cost metrics, alerts and dashboards.
Managing AI expenditure with budgets, alerts, and cost attribution across providers.
Collaborating with internal stakeholders to deliver features and create self-service tools.
Developing reusable AI capabilities: skills, plugins, and knowledge resources.
Driving automation and end-to-end ownership from design to deployment.