Technical Lead

• Design, build, and evolve backend services and APIs using C# / .NET, making pragmatic architectural decisions aligned to team and platform standards — directly and through AI agent delegation — ensuring that agent-generated code meets the same quality, security, and architectural standards as human-authored code.
• Guide the team through complex changes, refactors, and migrations, ensuring safe rollouts and production readiness.
• Lead frontend standards and evolution using Vue.js, supporting migration from legacy technologies.

Reliability & Operational Ownership

• Ensure reliability is built into sprint delivery through appropriate testing, observability, and operational readiness.
• Own incident response for the team’s areas: lead root-cause analysis and ensure learnings translate into durable fixes and runbooks.
• Champion monitoring, alerting, and sustainable on-call practices to support the platform’s mission-critical nature.

AI-Driven Engineering Practices

• Lead by example in responsible, effective use of AI developer tools to improve delivery speed, test coverage, and documentation.
• Establish and continuously refine team practices for AI-assisted development, focusing on collaboration, validation, and shared learning.
• Support engineers in using AI to explore edge cases, improve tests, and reduce rework without compromising quality or security.
• Drives a culture of active AI tool experimentation and rapid workflow adaptation within the team. Evaluates emerging AI engineering capabilities and makes pragmatic decisions about adoption.
• Ensure the team uses AI agents as primary workflow partners — not just inline code completion — for code generation, refactoring, test creation, and documentation. Set standards for AI task delegation, context management (e.g. repository instruction files, prompt templates), and output validation across the team.

Coaching, Collaboration & Quality

• Provide day-to-day technical leadership through pairing, code reviews, and design discussions.
• Use AI tools to augment code review effectiveness — including automated security scanning, consistency checking, and pre-review analysis — focusing human review effort on architectural judgement, business logic correctness, and design quality.
• Set and uphold clear quality bars for the team, ensuring testing and review depth matches customer and operational risk.
• Foster a collaborative, high-trust team environment aligned with Agile values and continuous improvement.
• Maintain high-quality architectural documentation, coding standards, and domain context that serves both human engineers and AI development tools — recognising that AI-assisted output quality is directly proportional to available context quality.

Values and Behaviours

Customer Centric

• Design and deliver features, integrations and reliability improvements that directly improve customer success.

Own It

• Take end-to-end responsibility for delivery, quality and technical debt, be accountable for successful outcomes.

One Team

• Collaborate tightly with Product and Platform to build trust, share goals, remove blockers and coach the team to succeed together.

Make It Count

• Prioritise solutions that improve reliability, scalability, model quality and long-term velocity with measurable results.

Challenge The Status Quo

• Continuously question assumptions and improve systems, tooling and processes

Success Measures

• The sprint team consistently meets commitments with high-quality, production-ready outcomes.
• Clear improvement in delivery predictability, reduced rework, and faster feedback cycles.
• Strong operational health in owned areas, with fewer repeat incidents and faster recovery.
• Drives measurable improvements in team throughput, quality, and lead time through the adoption and refinement of AI-native engineering workflows. Accountable for ensuring AI tools are embedded into the team’s definition of done — covering code generation, test creation, code review preparation, and documentation — with clear metrics tracking adoption and impact.

Training and Enablment

As part of the role you will be expected to continuously develop your skill set, take ownership of your growth, and help uplift the team through shared standards and mentoring.

• Structured onboarding plan with clear expectations for technical ownership and impact
• Dedicated time for learning and deep-dives into platform, reliability, and AI-enabled capabilities
• Budget for relevant certifications and conference attendance
• Peer collaboration and mentorship (both receiving and providing)
• Regular 1:1s and coaching from leadership, including career progression support
• Opportunities to lead discovery, POCs, and technical proposals
• Training and support on security, compliance, and operational excellence

Essential Skills and Experience

• Strong experience as a senior engineer or technical lead in Agile Scrum teams
• Proven ability to lead sprint delivery while remaining hands-on with C# / .NET (6–8) and modern frontend frameworks (Vue.js or similar).
• Demonstrated ability to shape work from ambiguity into clear plans, thin slices, and dependable delivery, communicating trade-offs and risks.
• Strong coaching, communication, and stakeholder collaboration skills (Product, Platform, Support/Operations).
• High bar for code quality, testing strategy (unit/integration/end-to-end), and production readiness appropriate to customer impact.
• Experience owning production systems where reliability, security, observability, and correctness matter (including incident response and root-cause analysis).
• Comfortable using AI developer tools responsibly (e.g., Copilot or similar), with clear validation practices and awareness of security/privacy considerations.
• Proven experience leading teams that use agentic AI workflows as a standard part of software delivery. Able to set team-wide standards for AI task delegation, context management, and output validation.

Desirable Skills and Experience

• Experience leading teams working on SaaS platforms in live customer environments.
• Strong experience with containerisation (Docker) and CI/CD pipelines, including improving build/release reliability and speed.
• Experience with cloud services and operational tooling (e.g., Azure/AWS), including monitoring and alerting.
• Hands-on exposure to AI-enabled systems (e.g., Azure OpenAI, vector search, RAG), including evaluation/guardrails and safe rollout practices.
• Track record modernising legacy codebases, reducing technical debt, and improving architectural fitness over time.
• Experience maintaining repository-level AI context and instruction files (e.g. CLAUDE.md,.cursorrules) that improve AI-assisted development quality and consistency across a codebase.
• Exposure to contact-centre, telephony, or CCaaS platforms operating in live customer environments.
• Awareness of emerging multi-agent development patterns and tools (e.g. parallel agent coordination, specialised review agents, automated CI/CD integration with agentic workflows) and interest in evaluating their applicability to team workflows.

Benefits And Perks

• 25 days annual