Delivery Practice Lead (Conversational AI & AI Agents)

QX Consulting Ltd is a focused conversational and agentic AI consultancy headquartered in the United Kingdom, with Subject Matter Experts across EMEA and a Centre of Excellence in Kraków, Poland. We help enterprises take Conversational AI from blueprint through MVP, production engineering, testing, deployment and hypercare, combining rigorous technical delivery with human-centred experience design and disciplined governance.

The Delivery Practice Lead is responsible for running and continuously improving QX’s implementation services capability. The role ensures that projects are delivered predictably, to a consistently high standard, and in alignment with QX’s delivery lifecycle and quality gates. This includes governance, delivery standards, resourcing discipline, and hands-on leadership across engagements. This role is a player/coach type role, with the need for the candidate to be comfortable rolling up their sleeves to support the wider team with client projects.

Key responsibilities

Practice leadership and standards

• Own and evolve QX implementation delivery playbooks, templates, and quality gates across the lifecycle (Project Charter, Blueprint, MVP, Production Engineering, Test & QA, UAT, Deploy, Hypercare, continual improvement).

• Establish consistent delivery governance, reporting, and risk management across engagements, ensuring stakeholders are clear on dependencies, decisions, and approvals.

Delivery oversight and engagement execution

• Provide delivery leadership across multiple client programmes, ensuring scope control, milestone achievement, and high-quality outcomes.

• Partner closely with Engagement Management, Project Managers (PMs), Architecture, Engineering, UX, and QA to maintain delivery discipline and resolve blockers quickly.

• Lead early project mobilisation: confirm objectives, define acceptance criteria and target metrics, validate assets and dependencies, and align all parties on ways of working.

Resourcing and capability management

• Plan and optimise utilisation across implementation resources (PMO, engineering, UX, QA), balancing delivery needs and commercial constraints.

• Coach and support team members on delivery best practice, including QA rigour, test strategy, and production readiness expectations.

Partner and vendor collaboration

• Operate effectively in a vendor, partner, client ecosystem, ensuring delivery alignment across technical owners (for example CCaaS, telephony, digital channels, identity and security).

• Support implementations on platforms such as Cognigy, Kore.ai, and Boost.ai, and contact centre ecosystems including Genesys, NICE, and Avaya, where relevant.

Continuous improvement

• Drive post go-live optimisation cycles using operational data, defect trends, transcript insights and test learnings to improve future delivery efficiency and solution outcomes.

• Raise delivery maturity over time through measured process change, tooling improvements, and repeatable patterns for integrations, testing and release readiness.

Skills and experience

Required

• Strong track record delivering customer-facing AI or contact-centre adjacent implementations (chat and or voice), including integrations and production releases.

• Practical experience owning delivery governance, risk, and quality across multiple engagements.

• Ability to translate between business objectives and technical execution, and to lead cross-functional teams without relying on hierarchy.

Preferred

• Experience working with low-code Conversational AI platforms, LLM-based approaches (including RAG), and contact centre deployments.

• Familiarity with partner-led delivery models and shared responsibility boundaries (for example client and partner owning upstream CCaaS or channel enablement).

• Confident communicator with senior client stakeholders, able to set expectations and hold the line on delivery discipline.

Reporting line and interfaces

• Reports directly to Co-Founders.

• Manages: Engagement Managers, Project Managers (PMs), AI Architects, AI Engineers, UX Specialists, QA, and partner/vendor teams.

Measures of success

• Delivery predictability: milestones met with controlled scope and managed risks.

• Profitability and delivery economics: Consistent achievement of target gross margin across implementation engagements through accurate estimating, effective resource utilisation, controlled scope (including change control), and reduced rework.

• Quality outcomes: fewer production defects, smoother UAT, strong production readiness.

• Practice maturity: improved templates, playbooks, and repeatable delivery patterns.

• Team performance: clear ways of working, effective resourcing, and improved capability.