Your responsibilities

Lead engineering delivery for AIOps and automation use cases, such as:
incident summarisation, alert correlation and routing suggestions, root cause and change risk assistants, communications and PIR drafting tools, patch/vulnerability intelligence.
Design and implement secure integrations across operational ecosystems — monitoring, ITSM, knowledge bases, configuration sources — ensuring least privilege, proper audit and resilient error handling.
Define measurable success indicators (MTTR reduction, alert noise reduction, adoption metrics, control quality) and instrument solutions to capture demonstrable impact.
Build reusable, approved AI patterns (“paved roads”) covering data redaction, grounding and citation, evaluation, monitoring and runbook standards.
Drive production governance — operational readiness, support model definition, dashboarding, incident playbooks, and rollback mechanisms for AI driven capabilities.
Provide technical leadership on automation engineering practices, standardisation and compliance with service management controls.
Participate in (or lead) production incident and problem management cycles to reduce recurrent failures.
Communicate clearly with senior stakeholders, translating engineering delivery into measurable business and operational outcome.
Note: Detailed project information will be shared during the recruitment process.

Our requirements

Proven software engineering background delivering production services and automation within operational environments.
Hands on experience with observability and operational data (logs, metrics, traces, alerts) and using these insights to prioritise engineering driven improvements.
Strong programming and scripting skills (Python preferred) with expertise in APIs, webhooks and event driven architecture.
Demonstrated DevSecOps mindset, continuous integration and delivery practices delivering small, safe, frequent changes.
Skilled at building production ready tooling with monitoring, audit trails and supportability built in.
Experience navigating complex organisations, driving delivery across multiple teams and priorities.
Practical Agile delivery knowledge (Scrum or Kanban) with excellent documentation and collaboration habits.
Fluent English (oral and written).
Experience implementing AI or GenAI solutions for operations, including retrieval augmented generation over KBs and runbooks, or incident and change risk assistants.
Understanding of Responsible AI, e.g. human in the loop design, auditability, AI incident response.
Demonstrated ability to publish engineering standards, reference implementations, and automation golden paths for organisation wide reuse.

Optional

Exposure to cloud environments (Azure, GCP, or AWS) and container orchestration (Kubernetes, Docker).
Familiarity with observability stacks (Geneos, Grafana, Prometheus, Loki, Splunk).
Experience with Python ML frameworks or APIs (OpenAI, Hugging Face, LangChain) in enterprise contexts.
Knowledge of ITIL process integration and governance in AI service delivery.

What we offer

Flexible cooperation model
Hybrid work setup – remote days available depending on the client’s arrangements - 6 days a week from the office in Kraków
Collaborative team culture – work alongside experienced professionals eager to share knowledge

Continuous development – access to training platforms and growth opportunities
Comprehensive benefits – including Interpolska Health Care, Multisport card, Warta Insurance, and more
High quality equipment – laptop and essential software provided

AI Solutions Engineer

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