What success looks like in this role:
POSITION SUMMARY
Unisys 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 Leadership
Own 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.
1. Lead hands-on design and code review across microservices, APIs, and AI-integrated application layers
2. Set and enforce engineering standards: code quality gates, test coverage thresholds, security practices, and CI/CD pipeline configuration in Azure DevOps (ADO)
3. Drive the architecture of cloud-native applications on Azure, including containerised services (Docker/Kubernetes), serverless functions, and event-driven patterns
4. Define and evolve the ADO engineering workflow: branching strategy, pull request standards, build pipelines, release gates, and sprint cadence
5. Own technical debt strategy — prioritising refactoring, deprecation, and modernisation alongside feature delivery
6. Ensure engineering documentation is rigorous: system design docs, API contracts, runbooks, and architectural decision records (ADRs)
2. AI/ML Engineering & AI-First Platform Development
Champion 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.
7. Architect and oversee the engineering of LLM-powered applications, including retrieval-augmented generation (RAG) pipelines, prompt engineering frameworks, and agentic orchestration patterns
8. Lead integration of Azure OpenAI, Azure AI Foundry, and related services into the SEA platform and client-facing solutions
9. Build robust MLOps practices: model versioning, A/B testing, performance monitoring, drift detection, and automated retraining pipelines
10. Evaluate and adopt AI frameworks including LangChain, Semantic Kernel, AutoGen, and vector database solutions (e.g., Azure AI Search, Pinecone, Weaviate)
11. Implement responsible AI engineering guardrails: content filtering, grounding, hallucination mitigation, audit logging, and bias monitoring
12. Develop AI-powered capabilities including enterprise copilots, intelligent workflow automation, predictive analytics, and NLP-driven knowledge retrieval
13. Establish engineering benchmarks for AI system performance: latency, token efficiency, recall/precision, and end-to-end task completion rates
3. Agile Engineering Delivery & Azure DevOps (ADO) Excellence
Agile is not a process overlay here — it is the engineering operating model. You will drive disciplined, high-velocity delivery through a mature ADO environment.
14. Own the ADO organisation: configure and govern boards, repos, pipelines, test plans, and artefact feeds across global engineering teams
15. Drive sprint planning, backlog refinement, and velocity management with a data-driven approach — using ADO analytics to surface delivery risk early
16. Implement 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)
17. Enforce definition-of-done standards that include test automation coverage, security review, performance benchmarking, and documentation
18. Lead post-sprint retrospectives that produce measurable process improvements, not just discussion
19. Champion DevSecOps: integrate security into every pipeline stage, from dependency scanning to infrastructure-as-code (IaC) validation
4. Application Architecture & Full-Stack Development Standards
Define and own the application development standards across the engineering organisation — from frontend component libraries to backend service contracts to data access patterns.
20. Establish full-stack development standards: frontend (React/TypeScript or equivalent), backend APIs (REST/GraphQL, .NET, Python, or Node.js), and data layer (SQL/NoSQL, Azure Cosmos DB, Azure SQL)
21. Architect multi-tenant SaaS patterns for enterprise platform delivery: isolation models, configuration-as-data, feature flagging, and tenant-level observability
22. Define API-first development practices: OpenAPI/Swagger standards, versioning strategy, backward compatibility policy, and developer portal tooling
23. Own integration architecture: enterprise system connectors (ServiceNow, Microsoft 365, Azure AD), event streaming (Azure Service Bus, Event Grid), and data pipeline engineering
24. Lead performance engineering: load testing frameworks, SLO/SLA definitions, capacity planning, and incident response engineering
25. Drive platform observability: structured logging, distributed tracing (Azure Monitor, Application Insights), alerting standards, and on-call engineering culture
5. Global Engineering Team Leadership & Talent Development
Build, lead, and grow a world-class engineering organisation distributed across multiple geographies. Engineer culture matters as much as technical output.
26. Hire, develop, and retain senior engineers, principal architects, and staff-level AI engineers — people who raise the bar across the organisation
27. Run rigorous engineering hiring: define levelling frameworks, design technical assessments, and conduct system design interviews personally
28. Build an engineering culture of ownership, peer code review, RFC processes, and blameless post-mortems
29. Establish engineering guilds or communities of practice for AI/ML, platform engineering, DevSecOps, and frontend disciplines
30. Create clear engineering career ladders with objective promotion criteria tied to technical impact, not tenure
31. Mentor senior engineers into principal and staff roles; actively build succession depth for all critical technical positions
6. Technology Strategy, Product Alignment & Stakeholder Engagement
Bridge the gap between deep engineering and business outcomes. Translate complex platform capabilities into compelling product roadmaps and executive narratives.
32. Define the multi-year engineering roadmap for SEA and Digital Workplace platforms, with clear investment rationale and build/buy/partner decisions
33. Partner with Product Management to shape prioritisation through an engineering lens: feasibility, velocity impact, and technical risk
34. Serve as the primary technical voice in executive and client conversations — able to demo prototypes, explain architectural trade-offs, and quantify platform value
35. Engage technology partners (Microsoft, ServiceNow, Google) at an engineering and product level to shape roadmap alignment and co-engineering opportunities
36. Represent Unisys engineering capabilities in RFP responses, client solution design sessions, and industry forums
You will be successful in this role if you have:
Required Experience
37. 12–18+ years of progressive software engineering and platform development experience, including 5+ years leading global engineering organisations
38. Demonstrable hands-on software development background: the ability to write, review, and debate code at a senior level is a baseline requirement for this role
39. Deep expertise in cloud-native application development on Microsoft Azure: App Services, Azure Kubernetes Service (AKS), Azure Functions, Cosmos DB, Azure SQL, Service Bus, and Event Grid
40. Production experience engineering AI/ML systems: LLM application development, RAG architectures, vector databases, prompt engineering, and MLOps pipelines
41. Expert-level Azure DevOps (ADO) proficiency: pipeline authoring (YAML), board configuration, test plan management, artefact management, and reporting
42. Strong command of modern software engineering practices: clean code principles, SOLID design, microservices patterns, API design, and test-driven development (TDD)
43. Proven track record delivering complex enterprise SaaS or platform products at global scale
44. Experience operating in enterprise IT environments with security, compliance, and governance requirements (e.g., SOC 2, ISO 27001, GDPR-aware engineering)
Preferred Experience
45. Prior engineering or product engineering leadership at a hyperscaler, enterprise software company, or large system integrator (e.g., Microsoft, IBM, Accenture, DXC, ServiceNow)
46. Hands-on experience with Microsoft AI/ML ecosystem: Azure OpenAI Service, Azure AI Foundry, Azure Machine Learning, Cognitive Services
47. Experience with AI agent frameworks: LangChain, Semantic Kernel, AutoGen, or comparable orchestration libraries
48. Background in digital workplace, enterprise service management, or employee experience platforms
49. Experience building and operating multi-region, multi-tenant enterprise platforms with high availability (99.9%+)
50. Familiarity with ServiceNow platform engineering and integration patterns
Technical Skills Profile
Candidates should be able to demonstrate depth across the following areas in interview:
51. Engineering depth: Languages & Frameworks
52. Python (AI/ML workloads), C# / .NET, TypeScript / React, and at least one additional backend language
53. Platform architecture: Cloud & Infrastructure
54. Azure-native architecture, AKS, IaC (Terraform or Bicep), container security, networking fundamentals
55. Applied AI systems: AI/ML Engineering
56. LLM APIs, RAG pipeline design, embedding models, vector search, agentic patterns, evaluation frameworks
57. Agile & DevOps: Engineering Delivery
58. ADO pipeline authoring, YAML CI/CD, GitFlow, release management, DevSecOps toolchain (SonarQube, Defender, Aqua)
Leadership & Behavioural Attributes
59. Engineering credibility: earns respect from senior individual contributors through technical depth, not just organisational authority
60. Builder mentality: energised by creating things from scratch, comfortable with ambiguity, and decisive under uncertainty
61. Outcome orientation: measures success by shipped software and measurable client impact, not activity or process compliance
62. Clear communicator: able to move fluently between whiteboard architecture discussions and board-level business conversations
63. Talent multiplier: invests disproportionately in developing engineers and creating an environment where great people want to stay
Education
64. Bachelor’s degree in Computer Science, Software Engineering, or a closely related engineering discipline — required
65. Master’s degree in Computer Science, AI/ML, or Engineering preferred; equivalent depth demonstrated through engineering output and technical leadership equally considered
HOW SUCCESS IS MEASURED
Dimension
Key Indicators
Engineering velocity & quality
Sprint velocity trends, deployment frequency, change failure rate, mean time to restore (MTTR) — tracked in ADO
AI platform adoption
SEA active usage, AI feature adoption rates, client satisfaction scores tied to AI-powered capabilities
Platform reliability
Uptime against SLO targets, P1/P2 incident count and MTTR, performance benchmark attainment
Talent & team health
Engineering retention rates, internal promotion rates, hiring close rates for senior/staff engineers, engagement scores
Innovation pipeline
Number of new AI capabilities shipped per quarter, patents filed, new platform revenue unlocked
Delivery predictability
Sprint commitment vs completion, release cadence consistency, scope creep rate per programme
Unisys is proud to be an equal opportunity employer that considers all qualified applicants without regard to age, caste, citizenship, color, disability, family medical history, family status, ethnicity, gender, gender expression, gender identity, genetic information, marital status, national origin, parental status, pregnancy, race, religion, sex, sexual orientation, transgender status, veteran status or any other category protected by law.