Role overview
Spydra is looking for an experienced technology leader to drive the successful execution of complex AI, data, blockchain, automation, and enterprise-platform initiatives.
This position sits at the intersection of business, product, engineering, architecture, and customer delivery. The person will convert business priorities into executable technology programs, establish clear delivery plans, address risks before they affect commitments, and ensure that solutions move reliably from design to production.
The role requires more than project tracking. The successful candidate will be expected to understand the technology being delivered, question assumptions, challenge designs where necessary, resolve cross-functional bottlenecks, and make practical decisions that protect customer outcomes.
The opportunity
You will lead a portfolio of strategic technology initiatives and ensure that each program has:
- Clearly defined business outcomes
- Realistic scope and delivery plans
- Strong technical foundations
- Accountable owners and delivery teams
- Transparent risk and dependency management
- Production-ready environments
- Measurable customer and business impact
You will work closely with senior leadership, customers, product managers, architects, engineering teams, cloud and infrastructure teams, and external partners.
Key areas of responsibilityTechnology program leadership
- Lead technology initiatives from discovery and planning through implementation, rollout, and production stabilization.
- Build integrated delivery plans covering product, engineering, data, infrastructure, security, testing, and deployment activities.
- Set milestones, delivery checkpoints, acceptance criteria, and readiness conditions for each initiative.
- Ensure teams remain focused on business-critical outcomes rather than activity-based progress.
- Review delivery status using tangible evidence such as working demonstrations, test results, deployment outputs, and production metrics.
- Identify delays early and implement recovery plans before commitments are affected.
- Adjust sequencing, resources, scope, or implementation approaches when required.
Technical oversight
- Review proposed architectures and engineering approaches for feasibility, scalability, security, maintainability, and operational readiness.
- Engage deeply with engineering teams to understand technical constraints and implementation risks.
- Challenge designs that create unnecessary complexity, delivery uncertainty, or long-term operational issues.
- Facilitate decisions where multiple technical approaches are available.
- Ensure that architecture choices are appropriate for the business requirement and delivery timeline.
- Confirm that solutions meet functional and non-functional expectations before progressing through major delivery gates.
- Promote practical engineering choices while maintaining essential quality and security standards.
AI, data, and platform delivery
- Oversee initiatives involving generative AI, machine learning, data platforms, enterprise integrations, workflow automation, and blockchain-based applications.
- Ensure AI systems are appropriately evaluated for output quality, reliability, security, privacy, bias, and operational performance.
- Review approaches involving model integration, retrieval-augmented generation, AI agents, vector databases, data pipelines, and model-serving infrastructure.
- Ensure that appropriate testing, observability, monitoring, human-review, and fallback mechanisms are incorporated into AI-enabled products.
- Work with platform teams to establish production-readiness and governance requirements.
- Support teams in moving AI and data solutions from proof of concept to scalable enterprise deployment.
Delivery governance and intervention
- Establish a delivery cadence that encourages timely decisions, clear ownership, and rapid issue resolution.
- Maintain visibility over risks, assumptions, dependencies, decisions, and unresolved actions.
- Assign clear owners and deadlines for critical issues.
- Investigate the underlying causes of delivery problems rather than relying only on status reporting.
- Step in directly when teams are blocked or commitments are at risk.
- Bring together the right stakeholders to resolve technical, business, commercial, or operational dependencies.
- Recommend corrective action where progress, quality, or accountability is insufficient.
- Escalate matters with clear options, implications, and recommended decisions.
Business and engineering alignment
- Translate business requirements into structured technology plans that engineering teams can execute.
- Clarify expected user behaviour, business rules, operational processes, and measurable success criteria.
- Resolve ambiguity before it results in development rework.
- Help business stakeholders understand technical constraints, dependencies, and implementation consequences.
- Present clear choices when trade-offs are required across scope, timeline, cost, quality, and risk.
- Ensure that delivered solutions address the intended customer or business problem.
- Maintain alignment between customer expectations, product priorities, and engineering capacity.
Partner and vendor management
- Coordinate onboarding of implementation partners, technology providers, consultants, and specialist vendors.
- Define technical access requirements, integration dependencies, delivery milestones, and productivity expectations.
- Track whether partners are contributing effectively within agreed timelines.
- Resolve delays involving access, infrastructure, environments, documentation, integration, or technical dependencies.
- Require improvement plans where partner performance is below expectations.
- Work with commercial and leadership teams where contractual or delivery interventions are required.
- Confirm partner and environment readiness before critical implementation stages.
Environment and release readiness
- Ensure development, testing, staging, and production environments are available in line with delivery plans.
- Establish readiness checklists for deployment, integration, security, performance, monitoring, and support.
- Coordinate resolution of infrastructure and environment-related dependencies.
- Ensure release decisions are supported by appropriate testing and operational evidence.
- Work with DevOps, cloud, security, and support teams to improve deployment predictability.
- Reduce avoidable delays caused by incomplete environments or unresolved production dependencies.
Decision-making expectations
The person in this role will be expected to:
- Take timely decisions within agreed commercial, technical, and governance boundaries.
- Recommend changes to delivery plans, scope, staffing, sequencing, or implementation approaches.
- Approve carefully controlled interim solutions where they are necessary to protect delivery.
- Require corrective action from internal teams and external partners.
- Prevent initiatives from progressing when critical security, reliability, or deployment conditions have not been met.
- Document important decisions and communicate their impact to relevant stakeholders.
- Involve senior leadership when decisions exceed agreed authority or materially affect customer and business commitments.
Candidate profile
You are likely to succeed in this role if you combine technical understanding with strong execution discipline.
You should be comfortable discussing architecture and engineering decisions with technical teams while also communicating delivery implications to business leaders and customers.
The role requires someone who is willing to take ownership, ask difficult questions, resolve conflict constructively, and remain focused on measurable outcomes.
Required experience
- Approximately 12–16 years or more of experience in technology delivery, engineering leadership, architecture, enterprise platforms, or digital transformation.
- Demonstrated experience leading complex programs involving multiple teams, systems, and stakeholders.
- Strong record of delivering enterprise technology solutions into production.
- Experience working directly with engineering, architecture, product, infrastructure, security, and business teams.
- Experience managing distributed teams, implementation partners, or technology vendors.
- Experience with AI, machine learning, data platforms, automation, cloud-native systems, or enterprise SaaS products.
- Understanding of the complete delivery lifecycle, including requirements, design, development, testing, deployment, and production support.
- Degree in Computer Science, Engineering, Information Technology, or a related discipline.
Relevant certifications in architecture, cloud platforms, program management, data engineering, cybersecurity, or artificial intelligence would be useful but are not essential.
Technical capabilities
The ideal candidate should have a sound understanding of several of the following areas:
- Generative AI and large language models
- Machine-learning systems and model deployment
- Retrieval-augmented generation and vector databases
- AI agents and workflow automation
- Data ingestion, ETL, streaming, and warehousing
- APIs and enterprise-system integrations
- Cloud architecture and containerized environments
- MLOps, DevOps, and release automation
- Application and infrastructure observability
- Security, privacy, access control, and data protection
- Distributed systems and blockchain platforms
- Scalability, performance, resilience, and availability
- Production monitoring and incident management
Hands-on development experience is valuable, but the primary requirement is the ability to assess technical approaches, identify delivery implications, and guide teams toward practical solutions.
