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.