Senior Automation Engineer

The

position is accountable for raising the level of automation across Falck's

digital infrastructure and for the technical direction of that automation

agenda. Based in Strategic Initiatives – new established part of Digital

Infrastructure & Operations. The role is equally advisory and hands-on,

combining advisory technical engagement with the infrastructure product teams

with direct, hands-on engineering delivery. It assesses automation maturity and

advises the product teams on engineering standards and roadmap direction

towards DevOps practices, without dictating those roadmaps. It drives the

building of Infrastructure-as-Code, CI/CD pipelines, API integrations and

machine learning-supported automation that connect existing infrastructure with

digital operations via collaboration with specialized teams. It also evaluates

the AI, ML and automation capabilities offered by infrastructure vendors, and

technically reviews and tests deliverables from Falck's outsourcing partner,

entering into dialogue with the vendor on the best solution.

A

key focus is to collaborate effectively with third-party providers and drive

results for Falck. These results include increased automation coverage across

the in-scope infrastructure domains, infrastructure changes delivered through

pipelines rather than manual execution, and documented recommendations that

inform architecture and sourcing decisions. Falck has outsourced key

infrastructure services, primarily to HCL.

• Bachelor's
• or Master's degree in computer science, software engineering, IT engineering or
• a comparable field, or equivalent documented practical experience
• Minimum 3 years in infrastructure
• engineering, systems architecture or DevOps, with a demonstrated transition
• into the AI space (for example LLM-based tooling and automation).
• Documented record of guiding teams through
• automation transformation programmes, in a technical lead or equivalent role.
• Working knowledge of Infrastructure-as-Code
• tools (Terraform, Ansible) and of CI/CD platforms; hands-on delivery experience
• is preferred.
• Experience with GitHub, Claude, and Copilot
• Studio.
• Programming and scripting in Python, which is
• required for the ML and automation work, and in Go, Bash or PowerShell.
• Familiarity with API-driven automation of
• network fabric, firewalls and IAM protocols (OAuth, SAML, Active Directory);
• hands-on experience with API integrations is not required, though knowledge is
• an advantage.
• ITSM and process automation through platform
• APIs, primarily ServiceNow: workflows, asset tracking, incident and change
• loops, service delivery metrics. Jira Service Management is an advantage.
• ML fundamentals, MLOps, and telemetry data
• analysis – log aggregation and AIOps.
• Enterprise cloud architecture on Azure, and
• containerisation and orchestration with Docker and Kubernetes. Experience with
• GCP is not required.
• Advising on technical direction and standards
• for engineers outside own reporting line, without formal authority over them.
• Advantage: work within a centralised strategy,
• architecture or Center of Excellence team structure.
• Advantage: automation within
• ITIL, asset management or vendor service delivery frameworks.
• .Interpersonal relations:
• consultative engagement with all Infrastructure Product Teams - including
• Project & Process Management - and with HCL, establishing agreement on
• shared standards without formal authority.
• Technical leadership: ability to
• advise on design decisions and encourage adoption of defined standards through
• code review, and to reach technical agreement across teams as an advisor rather
• than as Tech Lead across infrastructure as a whole.
• Analytic analysis: assessment of
• automation maturity, telemetry data and vendor capability against enterprise
• requirements.
• Flexibility: equal movement
• between advisory technical leadership and own hands-on code delivery, and
• adaptation to differing maturity levels across teams.
• Multitasking: prioritisation
• across parallel product teams, initiatives and Proofs of Concept.
• IT knowledge: infrastructure
• engineering, Infrastructure-as-Code, GitOps practice and machine learning
• applied to infrastructure operations.
• Communication: technical
• documentation of standards and decisions, and presentation of recommendations
• to technical and non-technical stakeholders in English.
• Knowledge transfer: coaching, pair
• programming and code review.