Technical Lead

About The Job:

• The Technical Lead is the senior architect and technical role in AiVA engagements.
• Once a client engagement is signed, this role owns the architectural pattern, the integration approach, and the technical quality of everything the AiVA pod builds.
• The role provides architectural direction, technical review, and escalation resolution.
• Accountable not for building skills but for the technical soundness of what gets built and liaising with the platform engineering team where necessary.

Overall aims:

• Technical architecture that supports the engagement outcomes: scalable, maintainable, secure, and defensible under client technical scrutiny.
• Consistent architectural pattern across the assigned pods.
• Sound technical decisions within the engagement, with clear architectural guidance to the AI Workflow Engineers.
• Technical soundness that supports both immediate engagement outcomes and long-term client platform investment.
• Architectural clarity that lets AI Workflow Engineers build at pace without technical rework.

Essential Job Functions:
Architecture & Technical Direction

• Define the architectural pattern for each engagement: skill decomposition, tool orchestration, data flow, integration approach, and deployment model.
• Ensure all architecture patterns are in line with security best practices and with client requirements. Designing client specific guardrails as appropriate.
• Review and sign off on AI Workflow Engineer builds against the agreed architectural pattern.
• Own integration architecture with client systems: authentication, data ingress, downstream deployment, and monitoring.
• Make platform-versus-custom decisions with the Head of Platform Engineering and Product where the engagement stretches platform capability.
• Design and document the target technical state, transition plan, and operational readiness criteria for handover to sustained delivery.
• Own the technical risk register for the engagement; surface and escalate technical risks early to the Delivery Principal.

Engagement Execution

• Work across multiple client engagements as the Technical Lead.
• Partner with the Delivery Principal on scope, timeline, and technical feasibility decisions.
• Present technical architecture to senior client stakeholders including Chief Technology Officers, technical partners, information security and enterprise architects.
• Specify all integrations and work with the platform development team to deliver integrations, connectivity, and upstream system integration.
• Guide the AI Workflow Engineer on architectural choices, prompt strategy, and evaluation design without taking over the build.
• Contribute technical input to commercial conversations; flag technical scope creep and platform-limit risks to the Delivery Principal and Client Partner early.

Technical Leadership

• Develop and mentor AI Workflow Engineers and Integration Engineers through the engagement; provide structured technical input to functional managers on capability development.

Continuous Improvement & Standards

• Contribute architectural patterns and technical playbooks to the Technical Lead community; refine standards based on engagement experience.
• Mentor newer Technical Leads as the function scales; contribute to capability development across the cohort.
• Ensure compliance with client requirements, data protection, security standards, and AiVA platform architectural standards across the engagement.

• Architecture and Technical Direction - Designs the architectural pattern for complex AI-enabled engagements. Makes scope, integration, and platform choices with confidence. Documents the target state and the transition plan for handover.
• Enterprise Integration Depth - Comfortable across enterprise integration patterns: authentication, data ingress and egress, downstream deployment, monitoring, and observability. Knows the trade-offs and defends them.
• AI Orchestration Literacy - Understands modern AI orchestration patterns: agent frameworks, tool integration, prompt strategy, and evaluation harnesses. Reads and reviews AI Workflow Engineer builds with technical rigour.
• Cloud and Platform Architecture - Deep working knowledge of cloud-native architecture, security patterns, and deployment models. Comfortable across Azure and AWS; familiar with AiVA platform architecture end-to-end.
• Cross-Pod Coordination - Works effectively across multiple client engagements (as appropriate) maintaining architectural consistency across pods without adding coordination overhead.
• Senior Client Presence - Presents technical decisions credibly to Chief Technology Officers, technical partners, client infosec, and enterprise architects. Defends design choices under senior technical scrutiny.
• Technical Mentorship - Develops AI Workflow Engineers and Integration Engineers through the engagement without taking over their work. Distinguishes teaching from doing.
• Technical Risk Management - Recognises technical risks early; surfaces them to the Delivery Principal; drives resolution without adding programme drama.

Personal Specifications/Attributes

• Senior technical presence; comfortable in client-facing rooms with senior technical stakeholders (Chief Technology Officers, enterprise architects, technical partners).
• Architect mindset with hands-on origins; can direct technical work without needing to do it, and knows when to intervene versus when to let a builder solve it.
• Comfortable operating across multiple engagements simultaneously; manages competing priorities without dropping either.
• Deep interest in AI-enabled automation architecture; keeps up with the platform, integration patterns, and the wider AI engineering community.
• Resilient under client technical pressure; defends design choices constructively rather than defensively.
• Continuous learner; adapts architectural patterns as the AiVA platform evolves.
• Quality-first orientation; treats architectural clarity, evaluation, and observability as first-class outcomes.
• Comfortable working across geographies (US, UK, India) and across sectors including legal and professional services.

Qualifications:

• 8+ years of software engineering, cloud architecture, or enterprise integration experience.
• Track record of leading complex technical architecture on client-facing delivery engagements; comfortable presenting to senior technical audiences.
• Demonstrated experience with AI or automation platforms at enterprise scale; familiarity with agent frameworks, LLM APIs, and orchestration patterns.
• Strong integration engineering background: authentication, data pipelines, deployment infrastructure, and monitoring.
• Cloud certification (Azure preferred, AWS, GCP relevant) or equivalent demonstrated cloud architecture experience.
• Bachelor’s degree in Computer Science, Engineering, or a related technical field, or equivalent experience.
• Comfortable working across geographies and time zones (US, UK, EMEA clients).
• Experience in legal, professional services, or knowledge-worker environments preferred; not required.