Senior Director, Global Head of Solution Development & AI Platforms

What success looks like in this role: POSITION SUMMARYUnisys is an AI-first company — and this role is at the heart of that transformation. The Senior Director, Global Head of Solution Development & AI Platforms is a deeply technical, hands-on engineering leader responsible for architecting, building, and scaling the software platforms that power Unisys Digital Workplace Solutions worldwide.This is not a purely strategic or managerial role. We are seeking a proven software engineering leader who can write and review code, set architectural direction, hold teams to engineering excellence, and accelerate the delivery of production-grade AI systems. You will own the full engineering lifecycle of the Service Experience Accelerator (SEA) — our flagship AI platform — alongside a broad portfolio of automation, data, and orchestration services delivered to enterprise clients globally.The ideal candidate has built and shipped complex distributed systems, led platform engineering organisations at scale (ideally in a hyperscaler or enterprise software environment), and is deeply fluent in modern AI/ML engineering, cloud-native development, and Agile delivery. You bring the hands-on credibility to earn the trust of senior engineers as well as the leadership range to operate at the executive level.1. Software Engineering & Platform Development LeadershipOwn the full software development lifecycle (SDLC) for the SEA platform and all Digital Workplace solution engineering. This means writing and reviewing code, setting language and framework standards, and making critical architecture decisions — not just overseeing from a distance.Lead hands-on design and code review across microservices, APIs, and AI-integrated application layersSet and enforce engineering standards: code quality gates, test coverage thresholds, security practices, and CI/CD pipeline configuration in Azure DevOps (ADO)Drive the architecture of cloud-native applications on Azure, including containerised services (Docker/Kubernetes), serverless functions, and event-driven patternsDefine and evolve the ADO engineering workflow: branching strategy, pull request standards, build pipelines, release gates, and sprint cadenceOwn technical debt strategy — prioritising refactoring, deprecation, and modernisation alongside feature deliveryEnsure engineering documentation is rigorous: system design docs, API contracts, runbooks, and architectural decision records (ADRs)2. AI/ML Engineering & AI-First Platform DevelopmentChampion Unisys’ AI-first strategy by engineering AI capabilities that are production-grade, observable, and scalable. This is applied AI engineering — not research or strategy in isolation.Architect and oversee the engineering of LLM-powered applications, including retrieval-augmented generation (RAG) pipelines, prompt engineering frameworks, and agentic orchestration patternsLead integration of Azure OpenAI, Azure AI Foundry, and related services into the SEA platform and client-facing solutionsBuild robust MLOps practices: model versioning, A/B testing, performance monitoring, drift detection, and automated retraining pipelinesEvaluate and adopt AI frameworks including LangChain, Semantic Kernel, AutoGen, and vector database solutions (e.g., Azure AI Search, Pinecone, Weaviate)Implement responsible AI engineering guardrails: content filtering, grounding, hallucination mitigation, audit logging, and bias monitoringDevelop AI-powered capabilities including enterprise copilots, intelligent workflow automation, predictive analytics, and NLP-driven knowledge retrievalEstablish engineering benchmarks for AI system performance: latency, token efficiency, recall/precision, and end-to-end task completion rates3. Agile Engineering Delivery & Azure DevOps (ADO) ExcellenceAgile is not a process overlay here — it is the engineering operating model. You will drive disciplined, high-velocity delivery through a mature ADO environment.Own the ADO organisation: configure and govern boards, repos, pipelines, test plans, and artefact feeds across global engineering teamsDrive sprint planning, backlog refinement, and velocity management with a data-driven approach — using ADO analytics to surface delivery risk earlyImplement and continuously improve CI/CD pipelines: automated testing, code quality scanning (SonarQube or equivalent), security scanning (SAST/DAST), and progressive deployment strategies (blue/green, canary)Enforce definition-of-done standards that include test automation coverage, security review, performance benchmarking, and documentationLead post-sprint retrospectives that produce measurable process improvements, not just discussionChampion DevSecOps: integrate security into every pipeline stage, from dependency scanning to infrastructure-as-code (IaC) validation4. Application Architecture & Full-Stack Development StandardsDefine and own the application development standards across the engineering organisation — from frontend component libraries to b