Technical Lead

Reference #: 8606 Location: Toronto or London (Hybrid – 3 days in office) Type: Sub-contract

This posting is for an incorporated contract engagement and is not an employment position.

Donna Cona Inc. is currently seeking a Technical Lead, for one of our key clients. We are looking for a hands-on Technical Lead and Senior Full-Stack Engineer who can build across the front end, back end, experience engine and AI / ML layers—and establish the engineering foundations required to deliver them reliably. In this role, you will remain hands on across the full technology stack—from React/Next.js user experiences and experience orchestration to Java / Spring Boot services, APIs, Kafka events and data integration using REST API via Apigee. You will also be able to establish cloud infrastructure and development environments and CI / CD pipelines, working with enterprise platform teams where shared services or controls are involved.

Candidate MUST have the following:

Over eight (8+) years building and operating complex enterprise digital products, including experience as a Technical Lead, Staff Engineer or senior full-stack developer;

Demonstrated full-stack depth: Strong, recent hands-on delivery in both modern front-end engineering and backend / service development, able to move confidently across layers rather than specializing in only one;

Deep experience with TypeScript, React and Next.js, including component architecture, state management, server-side rendering, accessibility, responsive design and front-end performance;

Strong backend engineering experience with Java and Spring Boot, including REST APIs, microservices, asynchronous processing, data access, automated testing and production troubleshooting;

Experience designing and integrating REST, GraphQL and event-driven services using clear contracts, authentication, resiliency patterns and graceful failure handling;

Hands-on experience establishing cloud infrastructure, containers, infrastructure as code using Terraform, source-control workflows and CI / CD pipelines. Azure experience is preferred, Kubernetes and AWS experience are valuable;

Strong quality-engineering capability, including test driven development, defining test strategy and automation across unit, component, contract, API, integration, end-to-end, accessibility, performance and security testing;

Practical use of AI-assisted engineering tools—such as Claude Code, GitHub Copilot or comparable tools—to accelerate software delivery, testing, debugging and documentation within approved security and code-review controls;

Hands-on experience delivering AI / ML-enabled product features using modern model platforms and frameworks, including retrieval, prompt engineering, structured outputs, tool use, evaluation, guardrails and production monitoring; and

Experience building reusable platform capabilities or modernizing fragmented applications while improving delivery speed, maintainability, reliability and customer outcomes.

Additional enterprise experience:

Experience with API gateways, Kafka or comparable event platforms, headless content, personalization, feature flags, experimentation, analytics, caching and relational or document databases;

Ability to integrate CIAM (Okta Auth0), entitlement, consent, privacy, security and audit controls into end-to-end solutions in a regulated environment;

Strong observability practice using logs, metrics, distributed tracing and alerting; familiarity with OpenTelemetry, Azure Monitor/Application Insights or comparable tooling;

Working knowledge of secure software supply chains, dependency management, secrets management and controls for AI-assisted development; and

Experience in financial services, insurance or another complex regulated environment is preferred.

Candidate should have the following demonstrated experience:

Create reusable components and orchestration services that assemble content, customer context, data, decisions and actions into consistent, personalized journeys;

Configure cloud infrastructure, development environments, infrastructure as code and CI / CD pipelines, partner with enterprise platform teams while remaining hands on in implementation and troubleshooting;

Implement automated unit, component, API, integration, end-to-end, accessibility, performance and security testing, with quality gates embedded in delivery pipelines;

Apply approved AI coding tools such as Claude Code to understand codebases, generate and refactor code, create tests, diagnose defects and automate routine development work—while maintaining human review, secure-development controls and accountability for every change;

Develop AI-assisted and agentic product features, including retrieval, prompt engineering, structured outputs, tool integration usin