Engineering Lead
Engineering Lead @The Dock
About The Dock
The Dock is Accenture's flagship R&D and Global Innovation Center, based in Dublin's Grand Canal Dock. As an incubation hub for the next generation of AI and GenAI agentic solutions, we bring together a global community of business strategists, designers, engineers, data scientists, and entrepreneurs to solve some of the world's hardest problems and build products and services that help industries adapt to rapid change.
Our teams operate across the full innovation spectrum — from early exploration through to real-world deployment — working in tight, multidisciplinary pods that turn ideas into things clients can actually use.
About the Role
As an Engineering Lead, you will be accountable for the engineering direction, delivery and effectiveness of a multidisciplinary engineering team, taking work from early technical discovery through implementation, deployment and ongoing evolution.
This is an engineering leadership role rather than a senior individual-contributor role. You will bring strong technical judgement and be close enough to the technology to challenge decisions, guide architecture and remove technical blockers, but your primary measure of success will be the effectiveness and output of the team you lead.
You will create clarity in ambiguous environments: shaping engineering approaches, establishing appropriate delivery structures, making pragmatic trade-offs and ensuring the team can turn evolving ideas into robust, deployable software.
You will lead engineers with different levels of experience, setting expectations, delegating ownership, providing coaching and feedback, and creating an environment in which engineers can make good technical decisions independently. You will help the team balance experimentation and speed with engineering quality, maintainability, security and operational readiness.
Working closely with Product, Design, Data Science and other stakeholders, you will represent engineering in planning and prioritisation discussions, surface risks and dependencies early, and ensure technical considerations inform product and delivery decisions.
You will remain technically credible and may occasionally work directly with the technology where it helps unblock the team or validate an approach. However, this is not a role where success is measured by the amount of code you personally produce.
Desired Skills & Experience
5–8 years’ experience as a software engineer delivering production‑grade systems.
Proven experience owning and delivering technical workstreams end‑to‑end, from early solution design through implementation and handover.
Strong software engineering fundamentals, including system design, APIs, data management, testing, and code quality.
Hands‑on experience building and integrating AI‑enabled capabilities into products or platforms (application and integration focused, not research).
Demonstrated ability to operate effectively in ambiguous problem spaces, translating evolving requirements into clear technical approaches.
Experience across the full software development lifecycle, including operational and support considerations.
Familiarity with cloud‑native environments and modern delivery tooling (CI/CD pipelines, containers, managed services).
Proven ability to make and clearly explain engineering trade‑offs across speed, quality, scalability, security, and cost.
Experience shaping technical approaches with awareness of delivery cost, sustainability, and downstream effort.
Strong experience working in cross‑functional teams, partnering with product, design, data science, and commercial colleagues.
Ability to communicate technical concepts clearly to non‑technical stakeholders.
Takes on technical ownership and decision‑making while remaining a hands‑on technology contributor.
Extras We’d Love to See
Experience delivering work in an innovation‑led, consulting, or product incubation environment.
Strong solution design skills — able to take a problem from concept through to a technical architecture that holds up under review.
Exposure to LLM‑based systems, ML platforms, or AI orchestration patterns used in real deployments.
Demonstrated contribution to reusable assets, accelerators, or shared engineering standards.
Experience operating within a large, matrixed organisation with shared ownership and dependencies.
Naturally curious about the wider AI landscape, and tests assumptions in code. Enjoys the loop between "what's out there" and "does it actually solve our problem" — and flags the gaps along the way.
Dublin
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