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CHI Software
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Program Engineering Lead

CHI Software
Work model
Not specified
Experience
7+ years
Employment
Contract
Compensation
Not disclosed
Technology signal
5 tags

Technology context

5

Parsed from the vacancy text; ordered by relevance to this role.

AI AgentsAIDevSecOpsCI/CDAutomation

Full listing

Role description

We're looking for a Program Engineering Lead to establish shared engineering standards and controls for how teams build, test, and release software. You'll help teams turn approved requirements and architecture into secure, deployable software they can support in production, and use evidence from testing and technical checks to guide release decisions.

Requirements

  • Experience overseeing 5 - 7 concurrent engineering workstreams or a programme of comparable scale, with complex integrations, APIs, data dependencies, shared platforms, legacy systems, and coordinated releases.
  • Deep knowledge of modern SDLC, DevSecOps, CI/CD, and automated testing, with experience applying consistent engineering standards across teams.
  • Practical experience with AI-assisted and agentic engineering, including AI agents in the SDLC, automated evaluations, guardrails, observability, and human approvals.
  • The ability to turn architecture and cross-system dependencies into engineering plans, integration milestones, and clear criteria for release readiness.

Key Responsibilities

  • Own the programme's engineering operating model, standards, and ways of working across delivery squads.
  • Set expectations for source control, code review, CI/CD, environments, security checks, deployment, observability, and rollback.
  • Work with Product, Architecture, and Testing to make BDD, Definition of Ready, and Definition of Done part of everyday engineering.
  • Lead technical readiness across squads, identifying gaps in skills, environments, tooling, automation, and integration before they delay delivery.
  • Coordinate Technical Leads, align teams on shared engineering patterns, and work with Architecture to manage cross-team integration risks. Squads make implementation decisions within the approved design.
  • Improve code quality and expand automated unit, component, contract, and integration testing. Make technical debt visible and retain test results and other evidence in the delivery pipeline.
  • Use delivery metrics such as flow, build and deployment health, defects, and rework to prioritise improvements.
  • Scale AI-assisted engineering across teams with clear guardrails, evaluation criteria, and accountable human approvals.