Senior Platform Engineer – DevOps, MLOps

Jobtailor; Finland; On-site; EUR 70,000 - 95,000; Full time

Job summary

Jobtailor is seeking a backend platform engineer to build and operate production-grade pipelines and tooling for CI/CD and AI-enabled features. You will design validation gates, eval flows, and telemetry pipelines to give developers fast and trustworthy signals before changes reach production.

You will collaborate with AI engineers, PMs, and other platform teams to create reusable workflows that scale, with an emphasis on maintainability, reliability, and observable release confidence across

Job description

Overview

Build the CI&D quality gates for backend and AI workflows so engineers get faster, more trustworthy signals before changes reach production.

Create reusable validation infrastructure for AI-powered backend features, including scenario-based evals, staging validation flows, generated test profiles, and higher-signal end-to-end checks.

Improve the feedback loop between development and production by connecting eval results, CI outcomes, runtime telemetry, and member-facing signals into one operational picture.

Help standardize the shared LLM delivery surface in practice: unified client patterns, trace capture, environment setup, and operational guardrails that feature squads can adopt without bespoke platform work.

Partner closely with backend engineers, AI engineers, PMs, and adjacent platform teams to turn release-confidence needs into reusable workflows that scale across the platform rather than one-off fixes for a single squad.

Responsibilities

• Build and operate production backend platforms, developer infrastructure, CI/CD systems, or internal engineering tooling used by real product teams.
• Design and implement backend software engineering solutions in a language such as Python, plus practical experience with cloud infrastructure, production debugging, and maintainable system design.
• Design validation or quality systems that teams trust: CI gates, eval flows, test infrastructure, telemetry pipelines, or release automation.
• Improve delivery confidence for other engineers, not just operating infrastructure manually.
• Support modern AI or LLM-backed systems in production through evals, tracing, rollout safety, review signals, or operational feedback loops.
• Collaborate and communicate effectively, translating platform pain points into practical systems used by many teams.